# How Does a Human-In-The-Loop System Improve the Accuracy of Automated Rfp Analysis over Time? ▴ Question

**Published:** 2025-08-09
**Categories:** Question

---

![A multi-layered, circular device with a central concentric lens. It symbolizes an RFQ engine for precision price discovery and high-fidelity execution](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-engine-for-precision-execution.jpg)

![A sophisticated dark-hued institutional-grade digital asset derivatives platform interface, featuring a glowing aperture symbolizing active RFQ price discovery and high-fidelity execution. The integrated intelligence layer facilitates atomic settlement and multi-leg spread processing, optimizing market microstructure for prime brokerage operations and capital efficiency](https://options.greeks.live/wp-content/uploads/2025/07/institutional-crypto-derivatives-rfq-engine-intelligence-layer.jpg)

## Concept

A [Human-in-the-Loop](https://prime.greeks.live/area/human-in-the-loop/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) (HITL) system for Request for Proposal (RFP) analysis represents a fundamental re-conception of the relationship between automated processing and human expertise. It establishes a cognitive partnership, a symbiotic learning architecture where machine-speed [data extraction](https://prime.greeks.live/area/data-extraction/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) and human-level contextual understanding perpetually refine one another. The system operates on a principle of continuous improvement, where the accuracy of the automated analysis is not a static benchmark but a dynamic capability that compounds over time.

This is achieved through a rigorously engineered [feedback loop](https://prime.greeks.live/area/feedback-loop/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite), a mechanism that transforms every human interaction into a precise training signal for the underlying [machine learning](https://prime.greeks.live/area/machine-learning/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) models. The objective is to build an analytical asset that learns the unique contours of an organization’s decision-making criteria.

The core of this architecture consists of three integrated components. First, an automated [extraction engine](https://prime.greeks.live/question/what-are-the-technical-requirements-for-automating-rfp-data-extraction-into-a-grc-system/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite), typically powered by advanced natural language processing (NLP), performs the initial high-volume parsing of RFP documents, identifying key terms, clauses, requirements, and risk factors. Second, a dedicated [human expert](https://prime.greeks.live/question/how-does-the-implementation-of-explainable-ai-change-the-role-of-the-human-trader-in-an-automated-environment/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) interface presents these automated outputs for review, specifically focusing on areas of ambiguity or low algorithmic confidence. Third, and most critically, a structured feedback mechanism captures the nuances of human judgment ▴ the corrections, validations, and re-classifications made by the expert ▴ and translates this intelligence into high-quality training data.

This data is then used to retrain and fine-tune the extraction engine, systematically improving its ability to handle similar complexities in the future. The system’s value is therefore measured not just by its initial accuracy, but by its rate of learning and adaptation.

> A Human-in-the-Loop framework transforms RFP analysis from a static task into a self-improving intelligent system.

![A sleek, multi-component device with a prominent lens, embodying a sophisticated RFQ workflow engine. Its modular design signifies integrated liquidity pools and dynamic price discovery for institutional digital asset derivatives](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-engine-microstructure-2.jpg)

## The Genesis of Compounding Accuracy

The improvement in accuracy over time originates from the system’s capacity to learn from the most challenging and context-dependent aspects of [RFP analysis](https://prime.greeks.live/area/rfp-analysis/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite). Automated systems excel at identifying well-defined, structured data points, such as deadlines or specific keywords. Their initial limitations appear when faced with semantic nuance, implied obligations, or unconventionally phrased requirements. A human expert, drawing on deep domain knowledge, effortlessly navigates these ambiguities.

The HITL system is engineered to capture this specific expertise. When an analyst corrects the model’s interpretation of a vaguely worded liability clause, they are not merely fixing a single error. They are creating a permanent, machine-readable data point that teaches the model how to recognize and correctly classify that specific pattern of legal language in all subsequent documents. This process, repeated across thousands of decisions, systematically encodes expert judgment into the algorithmic core of the system.

This compounding effect is a direct result of the iterative nature of the feedback loop. Each cycle of automated analysis, [human review](https://prime.greeks.live/question/what-role-does-human-oversight-play-in-mitigating-the-risks-of-using-ai-for-sensitive-tasks-like-rfp-review/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite), and [model retraining](https://prime.greeks.live/area/model-retraining/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) raises the baseline accuracy of the system. Consequently, the human expert is progressively freed from reviewing routine or obvious extractions. Their attention is focused on increasingly subtle and complex edge cases, the very instances that provide the most valuable training data for the model.

This creates a virtuous cycle ▴ as the model becomes more accurate, the human’s time becomes more valuable, as it is dedicated to solving higher-order problems that, in turn, generate the most potent data for further model improvement. The system evolves from a simple extraction tool into a sophisticated analytical partner that anticipates and correctly interprets intricate RFP components.

![A metallic disc, reminiscent of a sophisticated market interface, features two precise pointers radiating from a glowing central hub. This visualizes RFQ protocols driving price discovery within institutional digital asset derivatives](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocols-price-discovery.jpg)

![A polished metallic modular hub with four radiating arms represents an advanced RFQ execution engine. This system aggregates multi-venue liquidity for institutional digital asset derivatives, enabling high-fidelity execution and precise price discovery across diverse counterparty risk profiles, powered by a sophisticated intelligence layer](https://options.greeks.live/wp-content/uploads/2025/08/principal-rfq-hub-digital-asset-derivatives-liquidity-aggregation.jpg)

## Strategy

Deploying a [Human-in-the-Loop system](https://prime.greeks.live/question/how-does-a-human-in-the-loop-system-mitigate-concept-drift-in-financial-models/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) for RFP analysis requires a deliberate strategic framework that governs how human intelligence is injected into the automated workflow. The goal is to optimize the efficiency of the human expert, ensuring their cognitive effort is directed where it will produce the greatest improvement in system accuracy. Two primary strategies form the foundation of this operational design ▴ [Confidence-Based Review](https://prime.greeks.live/guide/why-defined-risk-spreads-are-the-key-to-trading-with-confidence/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) and Active Learning. These approaches determine which machine-generated data points are surfaced for human validation, moving from a broad safety net to a highly targeted method of model refinement.

![A sophisticated, illuminated device representing an Institutional Grade Prime RFQ for Digital Asset Derivatives. Its glowing interface indicates active RFQ protocol execution, displaying high-fidelity execution status and price discovery for block trades](https://options.greeks.live/wp-content/uploads/2025/07/principal-rfq-engine-for-digital-asset-derivatives-best-execution-1.jpg)

## Confidence-Based Review a Foundational Approach

The most direct strategy for HITL implementation is Confidence-Based Review. In this model, the automated extraction engine assigns a [confidence score](https://prime.greeks.live/area/confidence-score/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) to every piece of data it analyzes. This score represents the algorithm’s statistical certainty about the accuracy of its own output. A threshold is then established; any extraction falling below this confidence level is automatically flagged and routed to a human expert for verification.

For instance, the system might be 99.8% confident that it has correctly identified the submission deadline, but only 65% confident that it has correctly categorized a non-standard insurance requirement. The deadline would be accepted automatically, while the insurance clause would be queued for human review.

This strategy provides an essential layer of quality control, catching probable errors and preventing them from propagating into downstream workflows. The strategic variable in this model is the [confidence threshold](https://prime.greeks.live/guide/why-defined-risk-spreads-are-the-key-to-trading-with-confidence/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) itself, which can be adjusted based on risk tolerance and resource availability. A lower threshold increases the volume of reviews but catches more potential errors, while a higher threshold streamlines the process at the cost of potentially missing more subtle inaccuracies. Organizations often begin with a lower threshold to ensure high initial quality and gradually raise it as the model’s accuracy improves through repeated training cycles.

### Table 1 ▴ Impact of Confidence Threshold on Review Workload

| Confidence Threshold | Automated Acceptance Rate | Human Review Volume (per 1000 extractions) | Estimated Error Catch Rate |
| --- | --- | --- | --- |
| Low (e.g. 70%) | 75% | 250 | 98% |
| Medium (e.g. 85%) | 88% | 120 | 92% |
| High (e.g. 95%) | 96% | 40 | 81% |

![A sleek, multi-layered device, possibly a control knob, with cream, navy, and metallic accents, against a dark background. This represents a Prime RFQ interface for Institutional Digital Asset Derivatives](https://options.greeks.live/wp-content/uploads/2025/07/precision-control-interface-for-institutional-digital-asset-derivatives-rfq-protocols-and-prime-os.jpg)

## Active Learning the Path to Maximum Efficiency

[Active Learning](https://prime.greeks.live/area/active-learning/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) represents a more sophisticated and efficient strategic layer. While Confidence-Based Review focuses on likely errors, Active Learning focuses on informative opportunities. Instead of just showing the human expert what it thinks is wrong, the system strategically selects data points that it is most uncertain about.

The goal is to have the human label the examples that will provide the most new information to the model, thereby accelerating its learning curve. This is a crucial distinction; a low-confidence prediction might be an obvious error, but an instance of high uncertainty represents a genuine gap in the model’s understanding.

> Active Learning strategically queries the human expert to resolve the most informative ambiguities, maximizing the value of each review.

There are several techniques for implementing Active Learning, with “[uncertainty sampling](https://prime.greeks.live/area/uncertainty-sampling/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite)” being one of the most common. The system identifies extractions where the probabilities for the top two possible classifications are very close (e.g. a 51% chance it’s a “Technical Requirement” and a 49% chance it’s a “Security Protocol”). By presenting this specific case to the human, the system gains a definitive answer to a point of maximum confusion.

This targeted feedback is far more valuable for model training than confirming 100 high-confidence extractions. This strategy dramatically reduces the amount of data a human needs to label to achieve a high level of model accuracy, making it the superior long-term approach for building a truly intelligent system.

- **Initial Pass** ▴ The automated engine processes a batch of RFP documents, generating predictions and confidence scores for all relevant data points. 

- **Query Selection** ▴ An Active Learning algorithm analyzes the predictions and selects a small, high-value subset of data points for human review based on an uncertainty metric. 

- **Human Annotation** ▴ The expert analyst reviews the queried data points, correcting, validating, or re-classifying them through a specialized interface. 

- **Data Augmentation** ▴ The newly labeled, high-quality data from the human expert is added to the master training dataset. 

- **Model Retraining** ▴ The machine learning model is retrained using the augmented dataset, incorporating the new knowledge and improving its predictive accuracy for future tasks. 

![A precision-engineered component, like an RFQ protocol engine, displays a reflective blade and numerical data. It symbolizes high-fidelity execution within market microstructure, driving price discovery, capital efficiency, and algorithmic trading for institutional Digital Asset Derivatives on a Prime RFQ](https://options.greeks.live/wp-content/uploads/2025/07/high-fidelity-execution-engine-core-for-institutional-digital-asset-derivatives-rfq-protocol-price-discovery.jpg)

![A sophisticated digital asset derivatives trading mechanism features a central processing hub with luminous blue accents, symbolizing an intelligence layer driving high fidelity execution. Transparent circular elements represent dynamic liquidity pools and a complex volatility surface, revealing market microstructure and atomic settlement via an advanced RFQ protocol](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocols-high-fidelity-execution-framework-optimizing-market-microstructure-price-discovery-intelligence-layer.jpg)

## Execution

The execution of a Human-in-the-Loop system for RFP analysis is a detailed operational discipline. It involves establishing a robust data pipeline, defining clear roles for human analysts, implementing rigorous performance tracking, and creating a governance structure for continuous model improvement. This is the machinery that drives the compounding accuracy gains, transforming the strategic concept into a tangible organizational capability. The focus shifts from what the system does to precisely how it operates and evolves.

![A sleek, abstract system interface with a central spherical lens representing real-time Price Discovery and Implied Volatility analysis for institutional Digital Asset Derivatives. Its precise contours signify High-Fidelity Execution and robust RFQ protocol orchestration, managing latent liquidity and minimizing slippage for optimized Alpha Generation](https://options.greeks.live/wp-content/uploads/2025/07/high-fidelity-institutional-digital-asset-derivatives-execution-via-optimized-rfq-protocols-enhancing-market-microstructure.jpg)

## The Operational Playbook the Feedback Loop in Practice

The core of execution is the operational playbook that defines the end-to-end process of data flow and model refinement. This is a cyclical process, not a linear one, designed for perpetual enhancement.

- **Step 1 Ingestion and Automated Parsing** ▴ An RFP document, regardless of its format (PDF, DOCX, etc.), is ingested into the system. An Optical Character Recognition (OCR) layer first converts the document into machine-readable text. The [NLP](https://prime.greeks.live/area/nlp/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) extraction engine then performs its initial analysis, identifying and tagging hundreds or thousands of potential data points, from key dates and personnel requirements to complex legal and financial clauses. Each extraction is assigned a unique ID, a predicted label, and a confidence score. 

- **Step 2 Triage and Routing** ▴ Based on the chosen strategy (Confidence-Based Review or Active Learning), the system automatically triages the extractions. High-confidence items are passed directly to a structured database for use by the proposal team. Low-confidence or high-uncertainty items are routed to the Human Review Queue, along with relevant context from the source document. 

- **Step 3 Human Analysis and Annotation** ▴ A domain expert, such as a proposal manager or legal counsel, interacts with the review interface. For each flagged item, they are presented with the original text, the model’s suggested extraction and label, and a set of tools to act. They can accept the suggestion, correct the extracted text, or change the label entirely. This is the critical moment where human intelligence is injected. 

- **Step 4 Structured Feedback Capture** ▴ Every action taken by the human analyst is captured as structured data. A correction is not just a change; it is recorded as a “disagreement event” containing the initial prediction, the final human-verified value, the analyst’s ID, and a timestamp. This creates an immutable audit trail and a rich dataset for model retraining. 

- **Step 5 Model Retraining and Deployment** ▴ On a scheduled basis (e.g. weekly or bi-weekly), the accumulated structured feedback is used to retrain the NLP model. The model learns from its previous mistakes and the expert’s corrections. After passing a series of automated quality checks, the newly improved model is deployed into the production environment, raising the accuracy baseline for the next cycle. 

![Abstract depiction of an institutional digital asset derivatives execution system. A central market microstructure wheel supports a Prime RFQ framework, revealing an algorithmic trading engine for high-fidelity execution of multi-leg spreads and block trades via advanced RFQ protocols, optimizing capital efficiency](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-prime-os-rfq-execution-system-1.jpg)

## Quantitative Performance Dashboard

The effectiveness of the HITL system must be measured with quantitative rigor. A performance dashboard is essential for tracking progress and demonstrating ROI. This is not merely about tracking accuracy; it is about understanding the interplay between machine performance and human effort. This is where the visible intellectual grappling with the data becomes paramount.

It is tempting to look at a metric like “Human Intervention Rate” and assume the goal is to drive it to zero. However, a systems view reveals a more nuanced reality. A sudden drop in the intervention rate might indicate model improvement, or it could signal that the confidence threshold is set too high, allowing a new category of subtle errors to slip through undetected. The true goal is a steady, predictable decline in the intervention rate for known problem types, while the analyst’s effort is redirected to novel or more complex ambiguities, which might temporarily hold the overall intervention rate steady before it declines again. This reveals a system that is not just getting better at what it already knows, but is actively expanding the frontier of what it can understand.

The long paragraph that follows represents the kind of deep, almost obsessive focus required to truly architect a learning system. It is an authentic imperfection in its length because the details of the measurement framework are not a secondary concern; they are the very heart of the system’s ability to learn. Without this granular understanding, the “feedback loop” is just a concept. With it, it becomes a powerful engine for creating institutional knowledge.

We must track not just the rate of human correction, but the type of correction. Are analysts primarily fixing OCR errors, misclassified clauses, or incorrect entity extractions? By categorizing these corrections, we can diagnose specific weaknesses in the data processing pipeline. A high rate of OCR-related corrections might point to a need for a better upstream document scanning process, while a high rate of clause misclassification points directly to a need for more targeted training data for the NLP model.

Furthermore, we must analyze the performance delta between model versions. When a new model is deployed, we need to see a quantifiable reduction in errors for the specific types of problems the last batch of human feedback was meant to solve. This confirms that the learning is real and targeted. The dashboard, therefore, becomes a diagnostic tool for the health and evolution of the entire [cognitive architecture](https://prime.greeks.live/area/cognitive-architecture/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite), ensuring that every minute of human effort is translated into a measurable and compounding improvement in machine accuracy.

### Table 2 ▴ HITL System Performance Metrics Over Time

| Metric | Month 1 | Month 3 | Month 6 | Month 12 | Description |
| --- | --- | --- | --- | --- | --- |
| Overall Extraction Accuracy | 82.5% | 91.0% | 96.5% | 98.8% | Percentage of automated extractions that require no human correction. |
| Human Intervention Rate | 17.5% | 9.0% | 3.5% | 1.2% | Percentage of extractions flagged for mandatory human review. |
| Mean Time to Review (sec) | 45 | 32 | 25 | 18 | Average time an expert spends reviewing a single flagged item. |
| Model Confidence (Avg) | 0.84 | 0.90 | 0.95 | 0.98 | Average confidence score of automated extractions. |
| Novel Issue Rate | 5% | 2% | 0.8% | 0.3% | Percentage of reviews that identify a previously unseen type of error or ambiguity. |

![A precision-engineered teal metallic mechanism, featuring springs and rods, connects to a light U-shaped interface. This represents a core RFQ protocol component enabling automated price discovery and high-fidelity execution](https://options.greeks.live/wp-content/uploads/2025/07/rfq-execution-engine-component-for-institutional-digital-asset-derivatives.jpg)

## References

- Settles, Burr. “Active learning literature survey.” University of Wisconsin-Madison Department of Computer Sciences, 2009.

- Cohn, David A. et al. “Active learning with statistical models.” Journal of artificial intelligence research 4 (1996) ▴ 129-145.

- Monarch, Robert. “Human-in-the-Loop Machine Learning ▴ Active Learning and Annotation for Human-centered AI.” Manning Publications, 2021.

- Karim, Md Rezaul, et al. “Deep learning with human-in-the-loop for medical image analysis.” Intelligent Medicine 2.3 (2022) ▴ 156-170.

- Lewis, David D. and William A. Gale. “A sequential algorithm for training text classifiers.” Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval. 1994.

- Ein-Dor, Liat, et al. “Active learning for deep semantic parsing.” Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020.

- Hanafi, Maeda, et al. “SEER ▴ A Human-in-the-loop System for Progressively Learning and Refining Text Extraction Rules.” Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. 2022.

- CrowdReason. “Boosting Document AI Accuracy with Human-in-the-Loop.” iMerit Technology Services, Accessed August 7, 2025.

![A symmetrical, intricate digital asset derivatives execution engine. Its metallic and translucent elements visualize a robust RFQ protocol facilitating multi-leg spread execution](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-best-execution.jpg)

## Reflection

![A sophisticated teal and black device with gold accents symbolizes a Principal's operational framework for institutional digital asset derivatives. It represents a high-fidelity execution engine, integrating RFQ protocols for atomic settlement](https://options.greeks.live/wp-content/uploads/2025/08/institutional-digital-asset-derivatives-rfq-protocol-core-engine-for-best-execution.jpg)

## From Tool to Cognitive Asset

Ultimately, the integration of a Human-in-the-Loop system marks a strategic transition. It moves the function of RFP analysis from a repetitive, resource-intensive task to the development of a durable cognitive asset. The system becomes a living repository of the organization’s collective intelligence regarding proposals, risks, and requirements. The accumulated data and the refined models represent a form of institutional memory, one that is active, searchable, and constantly improving.

The true measure of its success is not just in the accuracy of a single analysis, but in the capability it builds within the organization. The framework provides a mechanism for continuously translating the most nuanced human expertise into a scalable, automated process, creating a persistent and defensible analytical advantage.

![A sophisticated modular apparatus, likely a Prime RFQ component, showcases high-fidelity execution capabilities. Its interconnected sections, featuring a central glowing intelligence layer, suggest a robust RFQ protocol engine](https://options.greeks.live/wp-content/uploads/2025/07/institutional-rfq-engine-core-for-digital-asset-derivatives-high-fidelity-execution-protocols.jpg)

## Glossary

<a href="https://prime.greeks.live/area/human-in-the-loop/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A central luminous, teal-ringed aperture anchors this abstract, symmetrical composition, symbolizing an Institutional Grade Prime RFQ Intelligence Layer for Digital Asset Derivatives. Overlapping transparent planes signify intricate Market Microstructure and Liquidity Aggregation, facilitating High-Fidelity Execution via Automated RFQ protocols for optimal Price Discovery](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-execution-prime-os-intelligence-layer.jpg)

## Human-In-The-Loop

Meaning ▴ Human-in-the-Loop (HITL) designates a system architecture where human cognitive input and decision-making are intentionally integrated into an otherwise automated workflow. </a><a href="https://prime.greeks.live/area/data-extraction/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A central teal sphere, representing the Principal's Prime RFQ, anchors radiating grey and teal blades, signifying diverse liquidity pools and high-fidelity execution paths for digital asset derivatives. Transparent overlays suggest pre-trade analytics and volatility surface dynamics](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-and-market-microstructure.jpg)

## Data Extraction

Meaning ▴ Data Extraction defines the systematic process of retrieving specific information from diverse, often disparate, sources to convert it into a structured format suitable for computational processing and analytical consumption. </a><a href="https://prime.greeks.live/area/machine-learning/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A glowing green ring encircles a dark, reflective sphere, symbolizing a principal's intelligence layer for high-fidelity RFQ execution. It reflects intricate market microstructure, signifying precise algorithmic trading for institutional digital asset derivatives, optimizing price discovery and managing latent liquidity](https://options.greeks.live/wp-content/uploads/2025/07/core-institutional-digital-asset-derivatives-rfq-execution-system-hub.jpg)

## Machine Learning

Meaning ▴ Machine Learning refers to computational algorithms enabling systems to learn patterns from data, thereby improving performance on a specific task without explicit programming. </a><a href="https://prime.greeks.live/area/feedback-loop/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A precision institutional interface features a vertical display, control knobs, and a sharp element. This RFQ Protocol system ensures High-Fidelity Execution and optimal Price Discovery, facilitating Liquidity Aggregation](https://options.greeks.live/wp-content/uploads/2025/08/institutional-digital-asset-derivatives-rfq-execution-platform-interface.jpg)

## Feedback Loop

Meaning ▴ A Feedback Loop defines a system where the output of a process or system is re-introduced as input, creating a continuous cycle of cause and effect. </a><a href="https://prime.greeks.live/question/what-are-the-technical-requirements-for-automating-rfp-data-extraction-into-a-grc-system/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![An exposed high-fidelity execution engine reveals the complex market microstructure of an institutional-grade crypto derivatives OS. Precision components facilitate smart order routing and multi-leg spread strategies](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-execution-engine-microstructure-2.jpg)

## Extraction Engine

Automating RFP data extraction requires an API-driven architecture integrating NLP for interpretation with a GRC system for structured risk analysis. </a><a href="https://prime.greeks.live/question/how-does-the-implementation-of-explainable-ai-change-the-role-of-the-human-trader-in-an-automated-environment/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A precise geometric prism reflects on a dark, structured surface, symbolizing institutional digital asset derivatives market microstructure. This visualizes block trade execution and price discovery for multi-leg spreads via RFQ protocols, ensuring high-fidelity execution and capital efficiency within Prime RFQ](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocols-precision-execution.jpg)

## Human Expert

XAI re-architects the trader's role from market executor to a strategic manager of a transparent, AI-driven decision-making system. </a><a href="https://prime.greeks.live/area/rfp-analysis/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![An abstract composition of interlocking, precisely engineered metallic plates represents a sophisticated institutional trading infrastructure. Visible perforations within a central block symbolize optimized data conduits for high-fidelity execution and capital efficiency](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocols-multi-leg-execution-layer.jpg)

## Rfp Analysis

Meaning ▴ RFP Analysis defines a structured, systematic evaluation process for prospective technology and service providers within the institutional digital asset derivatives landscape. </a><a href="https://prime.greeks.live/area/model-retraining/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A reflective disc, symbolizing a Prime RFQ data layer, supports a translucent teal sphere with Yin-Yang, representing Quantitative Analysis and Price Discovery for Digital Asset Derivatives. A sleek mechanical arm signifies High-Fidelity Execution and Algorithmic Trading via RFQ Protocol, within a Principal's Operational Framework](https://options.greeks.live/wp-content/uploads/2025/07/precision-execution-digital-asset-derivatives-rfq-protocols-principal-trading-infrastructure-automated-liquidity-aggregation-intelligence-layer.jpg)

## Model Retraining

Meaning ▴ Model Retraining refers to the systematic process of updating the parameters, and potentially the structure, of a deployed machine learning model using new data to sustain its predictive accuracy and ensure its continued relevance in dynamic environments. </a><a href="https://prime.greeks.live/question/what-role-does-human-oversight-play-in-mitigating-the-risks-of-using-ai-for-sensitive-tasks-like-rfp-review/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A central core represents a Prime RFQ engine, facilitating high-fidelity execution. Transparent, layered structures denote aggregated liquidity pools and multi-leg spread strategies](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-rfq-engine-for-institutional-digital-asset-derivatives-execution.jpg)

## Human Review

Human oversight integrates contextual intelligence and ethical judgment into AI-driven RFP reviews, mitigating risk and ensuring strategic alignment. </a><a href="https://prime.greeks.live/question/how-does-a-human-in-the-loop-system-mitigate-concept-drift-in-financial-models/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A metallic, modular trading interface with black and grey circular elements, signifying distinct market microstructure components and liquidity pools. A precise, blue-cored probe diagonally integrates, representing an advanced RFQ engine for granular price discovery and atomic settlement of multi-leg spread strategies in institutional digital asset derivatives](https://options.greeks.live/wp-content/uploads/2025/08/institutional-digital-asset-derivatives-rfq-protocol-system-facilitating-high-fidelity-execution-price-discovery.jpg)

## Human-In-The-Loop System

A Human-in-the-Loop system institutionalizes expert judgment to continuously retrain models on uncertain data, mitigating drift. </a><a href="https://prime.greeks.live/guide/why-defined-risk-spreads-are-the-key-to-trading-with-confidence/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A sleek, institutional-grade device, with a glowing indicator, represents a Prime RFQ terminal. Its angled posture signifies focused RFQ inquiry for Digital Asset Derivatives, enabling high-fidelity execution and precise price discovery within complex market microstructure, optimizing latent liquidity](https://options.greeks.live/wp-content/uploads/2025/08/institutional-digital-asset-derivatives-rfq-execution-terminal-facilitating-high-fidelity-price-discovery-and-optimal-liquidity-aggregation.jpg)

## Confidence-Based Review

Trade with the certainty of defined outcomes, transforming market volatility into a strategic advantage. </a><a href="https://prime.greeks.live/area/confidence-score/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A precision-engineered RFQ protocol engine, its central teal sphere signifies high-fidelity execution for digital asset derivatives. This module embodies a Principal's dedicated liquidity pool, facilitating robust price discovery and atomic settlement within optimized market microstructure, ensuring best execution](https://options.greeks.live/wp-content/uploads/2025/08/institutional-rfq-engine-for-digital-asset-derivatives-best-execution.jpg)

## Confidence Score

Meaning ▴ A Confidence Score represents a derived quantitative metric indicating the statistical certainty or reliability associated with a specific algorithmic prediction, trade signal, or system output, often expressed as a probability or a scaled index, critical for assessing the integrity of automated decisioning within high-frequency trading or risk management frameworks for institutional digital asset derivatives. </a><a href="https://prime.greeks.live/guide/why-defined-risk-spreads-are-the-key-to-trading-with-confidence/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![An institutional-grade platform's RFQ protocol interface, with a price discovery engine and precision guides, enables high-fidelity execution for digital asset derivatives. Integrated controls optimize market microstructure and liquidity aggregation within a Principal's operational framework](https://options.greeks.live/wp-content/uploads/2025/07/proprietary-institutional-digital-asset-derivatives-rfq-protocol-engine-facilitating-high-fidelity-execution-and-granular-market-microstructure-analysis-for-prime-os.jpg)

## Confidence Threshold

Trade with the certainty of defined outcomes, transforming market volatility into a strategic advantage. </a><a href="https://prime.greeks.live/area/active-learning/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A sleek spherical mechanism, representing a Principal's Prime RFQ, features a glowing core for real-time price discovery. An extending plane symbolizes high-fidelity execution of institutional digital asset derivatives, enabling optimal liquidity, multi-leg spread trading, and capital efficiency through advanced RFQ protocols](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-platform-for-high-fidelity-execution-and-optimal-liquidity.jpg)

## Active Learning

Meaning ▴ Active Learning denotes an iterative machine learning paradigm where an algorithm strategically selects specific data points from which to acquire labels, aiming to achieve high accuracy with minimal training data. </a><a href="https://prime.greeks.live/area/uncertainty-sampling/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![Two distinct ovular components, beige and teal, slightly separated, reveal intricate internal gears. This visualizes an Institutional Digital Asset Derivatives engine, emphasizing automated RFQ execution, complex market microstructure, and high-fidelity execution within a Principal's Prime RFQ for optimal price discovery and block trade capital efficiency](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-automated-rfq-execution-microstructure-revealing-principal-prime-os-intelligence-layer-for-high-fidelity-execution-and-capital-efficiency.jpg)

## Uncertainty Sampling

Meaning ▴ Uncertainty Sampling identifies data points where a machine learning model exhibits the lowest confidence in its predictions, prioritizing these instances for human annotation or expert review. </a><a href="https://prime.greeks.live/area/nlp/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A sleek device showcases a rotating translucent teal disc, symbolizing dynamic price discovery and volatility surface visualization within an RFQ protocol. Its numerical display suggests a quantitative pricing engine facilitating algorithmic execution for digital asset derivatives, optimizing market microstructure through an intelligence layer](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-for-digital-asset-rfq-execution-system-with-smart-liquidity-routing.jpg)

## Nlp

Meaning ▴ Natural Language Processing (NLP) is a computational methodology for analyzing and interpreting human language. </a><a href="https://prime.greeks.live/area/cognitive-architecture/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A sophisticated RFQ engine module, its spherical lens observing market microstructure and reflecting implied volatility. This Prime RFQ component ensures high-fidelity execution for institutional digital asset derivatives, enabling private quotation for block trades](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-engine-for-best-execution-protocol.jpg)

## Cognitive Architecture

Meaning ▴ A Cognitive Architecture defines the foundational structure and operational principles of an intelligent system engineered to process information, execute complex reasoning, and make autonomous decisions within a specified domain. </a>

**Tags:** [Machine Learning](https://options.greeks.live/area/machine-learning/), [Human-in-the-Loop](https://options.greeks.live/area/human-in-the-loop/), [Confidence Score](https://options.greeks.live/area/confidence-score/), [Data Annotation](https://options.greeks.live/area/data-annotation/), [NLP](https://options.greeks.live/area/nlp/), [RFP Analysis](https://options.greeks.live/area/rfp-analysis/)	

## Discover More

- 
### [How Can Machine Learning Algorithms Predict and Prevent Block Trade Reporting Discrepancies?](https://options.greeks.live/question/how-can-machine-learning-algorithms-predict-and-prevent-block-trade-reporting-discrepancies/)
![A central toroidal structure and intricate core are bisected by two blades: one algorithmic with circuits, the other solid. This symbolizes an institutional digital asset derivatives platform, leveraging RFQ protocols for high-fidelity execution and price discovery. It optimizes market microstructure for capital efficiency, managing multi-leg spreads and latent liquidity within a Principal's operational framework.](https://options.greeks.live/wp-content/uploads/2025/07/digital-asset-derivatives-rfq-execution-hub-integrating-algorithmic-intelligence-for-high-fidelity-price-discovery-and-liquidity-aggregation.jpg)

Machine learning algorithms predict and prevent block trade reporting discrepancies by discerning subtle patterns and anomalies for proactive intervention.

- 
### [How Can Machine Learning Models Differentiate between Various Types of Settlement Fails?](https://options.greeks.live/question/how-can-machine-learning-models-differentiate-between-various-types-of-settlement-fails/)
![Abstractly depicting an Institutional Digital Asset Derivatives ecosystem. A robust base supports intersecting conduits, symbolizing multi-leg spread execution and smart order routing. Floating spheres represent diverse liquidity pools and crypto derivatives, emphasizing market microstructure and capital efficiency via RFQ protocols.](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-execution-engine-optimization.jpg)

ML models differentiate settlement fails by classifying trades based on learned patterns in transactional, counterparty, and market data.

- 
### [What Is the Role of Automation in Tracking and Analyzing RFP KPIs?](https://options.greeks.live/question/what-is-the-role-of-automation-in-tracking-and-analyzing-rfp-kpis/)
![A sleek, circular, metallic-toned device features a central, highly reflective spherical element, symbolizing dynamic price discovery and implied volatility for Bitcoin options. This private quotation interface within a Prime RFQ platform enables high-fidelity execution of multi-leg spreads via RFQ protocols, minimizing information leakage and slippage.](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-institutional-digital-asset-derivatives-rfq-execution-hub.jpg)

Automation transforms RFP analysis from a retrospective audit into a real-time system for strategic value acquisition and risk mitigation.

- 
### [How Does Natural Language Processing Help in Extracting Contractual Obligations from Rfp Documents?](https://options.greeks.live/question/how-does-natural-language-processing-help-in-extracting-contractual-obligations-from-rfp-documents/)
![Precision-engineered multi-vane system with opaque, reflective, and translucent teal blades. This visualizes Institutional Grade Digital Asset Derivatives Market Microstructure, driving High-Fidelity Execution via RFQ protocols, optimizing Liquidity Pool aggregation, and Multi-Leg Spread management on a Prime RFQ.](https://options.greeks.live/wp-content/uploads/2025/07/precision-engineered-multi-vane-system-illustrating-institutional-digital-asset-derivatives-rfq-protocols-market-microstructure-optimized-liquidity-aggregation-prime-os-high-fidelity-execution.jpg)

NLP transforms RFP documents into structured data, enabling automated extraction and management of contractual obligations for enhanced risk control.

- 
### [How Can Post Trade Tca Data Be Used to Refine Pre Trade Algorithmic Selection Models?](https://options.greeks.live/question/how-can-post-trade-tca-data-be-used-to-refine-pre-trade-algorithmic-selection-models/)
![A luminous central hub with radiating arms signifies an institutional RFQ protocol engine. It embodies seamless liquidity aggregation and high-fidelity execution for multi-leg spread strategies. Varied quadrants represent diverse market microstructure, optimizing capital efficiency within a principal's operational framework, mitigating counterparty risk.](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-hub-for-optimal-liquidity-aggregation.jpg)

Post-trade TCA data provides the empirical feedback necessary to systematically evolve pre-trade algorithmic selection into an adaptive, predictive system.

- 
### [How Do You Balance Model Sensitivity with the Risk of Generating Too Many False Positives for Analysts?](https://options.greeks.live/question/how-do-you-balance-model-sensitivity-with-the-risk-of-generating-too-many-false-positives-for-analysts/)
![Beige cylindrical structure, with a teal-green inner disc and dark central aperture. This signifies an institutional grade Principal OS module, a precise RFQ protocol gateway for high-fidelity execution and optimal liquidity aggregation of digital asset derivatives, critical for quantitative analysis and market microstructure.](https://options.greeks.live/wp-content/uploads/2025/07/principal-os-rfq-engine-for-digital-asset-derivatives-high-fidelity-execution-optimal-liquidity-aggregation.jpg)

Balancing model sensitivity and false positives is a dynamic calibration of a system's risk aperture to optimize analyst capacity.

- 
### [How Can Machine Learning Be Used to Optimize the Thresholds in a Smart Order Routing System?](https://options.greeks.live/question/how-can-machine-learning-be-used-to-optimize-the-thresholds-in-a-smart-order-routing-system/)
![A stylized abstract radial design depicts a central RFQ engine processing diverse digital asset derivatives flows. Distinct halves illustrate nuanced market microstructure, optimizing multi-leg spreads and high-fidelity execution, visualizing a Principal's Prime RFQ managing aggregated inquiry and latent liquidity.](https://options.greeks.live/wp-content/uploads/2025/07/principal-rfq-execution-engine-optimizing-digital-asset-derivatives-market-microstructure-for-institutional-liquidity-aggregation.jpg)

ML optimizes SOR thresholds by using predictive and reinforcement learning to dynamically adapt to real-time market data for superior execution.

- 
### [What Are the Best Practices for Incorporating Expert Feedback into Model Retraining?](https://options.greeks.live/question/what-are-the-best-practices-for-incorporating-expert-feedback-into-model-retraining/)
![Abstract forms illustrate a Prime RFQ platform's intricate market microstructure. Transparent layers depict deep liquidity pools and RFQ protocols. Sharp elements symbolize high-fidelity execution and multi-leg spread pricing for institutional digital asset derivatives, ensuring atomic settlement and precise price discovery.](https://options.greeks.live/wp-content/uploads/2025/07/high-fidelity-rfq-for-institutional-digital-asset-derivatives-primeos.jpg)

A systematic framework for translating expert intuition into quantitative model enhancements, driving continuous performance improvement.

- 
### [How Does the System Balance the Trade-Off between Threat Detection and Operational Disruption from False Positives?](https://options.greeks.live/question/how-does-the-system-balance-the-trade-off-between-threat-detection-and-operational-disruption-from-false-positives/)
![A modular, dark-toned system with light structural components and a bright turquoise indicator, representing a sophisticated Crypto Derivatives OS for institutional-grade RFQ protocols. It signifies private quotation channels for block trades, enabling high-fidelity execution and price discovery through aggregated inquiry, minimizing slippage and information leakage within dark liquidity pools.](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-system-for-optimal-execution-1.jpg)

A system balances threat detection and disruption by layering predictive analytics over risk-based rules, dynamically calibrating alert sensitivity.

---

## Raw Schema Data

```json
{
    "@context": "https://schema.org",
    "@type": "BreadcrumbList",
    "itemListElement": [
        {
            "@type": "ListItem",
            "position": 1,
            "name": "Home",
            "item": "https://options.greeks.live"
        },
        {
            "@type": "ListItem",
            "position": 2,
            "name": "Question",
            "item": "https://options.greeks.live/question/"
        },
        {
            "@type": "ListItem",
            "position": 3,
            "name": "How Does a Human-In-The-Loop System Improve the Accuracy of Automated Rfp Analysis over Time?",
            "item": "https://options.greeks.live/question/how-does-a-human-in-the-loop-system-improve-the-accuracy-of-automated-rfp-analysis-over-time/"
        }
    ]
}
```

```json
{
    "@context": "https://schema.org",
    "@type": "Organization",
    "@id": "https://greeks.live/#organization",
    "name": "Greeks.Live",
    "url": "https://greeks.live/",
    "description": "Greeks.live is the definitive toolkit for the professional options trader, dedicated to empowering our users with institutional-grade data, advanced analytics, and a comprehensive suite of trading tools designed to provide a decisive edge in the market. Every article and guide published under the Greeks.live name is a product of our deep commitment to data-driven analysis and our mission to support the global trading community.",
    "email": "support@greeks.live",
    "logo": {
        "@type": "ImageObject",
        "url": "https://options.greeks.live/wp-content/uploads/2025/07/Greeks.Live-Logo-Icon.svg"
    },
    "hasPart": [
        {
            "@type": "WebSite",
            "name": "About Us",
            "url": "https://about.greeks.live"
        },
        {
            "@type": "WebSite",
            "name": "App Download Page",
            "url": "https://about.greeks.live/#/download/basic_trade"
        },
        {
            "@type": "WebSite",
            "name": "Newcomers' Guide",
            "url": "https://twisty-sign-c8b.notion.site/Guide-for-Greeks-live-X-Deribit-Newbies-5a08a3052822427d800af6eaf3704c8d"
        },
        {
            "@type": "WebSite",
            "name": "Knowledge Base",
            "url": "https://linktr.ee/greeks.live"
        },
        {
            "@type": "WebSite",
            "name": "Option Terminology",
            "url": "https://greekslive.notion.site/88dfc00e7c584d4d8969b6302a79a12f?pvs=4"
        },
        {
            "@type": "WebSite",
            "name": "Terms of Service",
            "url": "https://greeks.live/#/tos"
        },
        {
            "@type": "WebSite",
            "name": "Disclaimer",
            "url": "https://greeks.live/#/disclaimer"
        }
    ],
    "sameAs": [
        "https://x.com/greekslive",
        "https://youtube.com/@greeks_live?si=-KjT0Lkw4WUCI0tD",
        "https://t.me/greekslive_notifications",
        "https://t.me/greekslive",
        "https://pinterest.com/greekslive/"
    ],
    "makesOffer": [
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "RFQ Platform",
                "url": "https://rfq.greeks.live",
                "description": "Execute institutional-grade block trades for crypto options and futures. This platform provides deep, over-the-counter (OTC) liquidity via a professional Request for Quote system.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        },
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "Lending Tools",
                "url": "https://lend.greeks.live",
                "description": "Manage crypto lending and borrowing activities to earn yield or secure loans. This suite of tools helps users analyze interest rates and manage collateralized debt positions.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        },
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "Data Lab",
                "url": "https://greeks.live/#/deribit/tools/datalab/BTC",
                "description": "Analyze historical and real-time options data with advanced metrics. This tool is designed for backtesting strategies and identifying market trends through comprehensive data visualization.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        },
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "Profit & Loss Analysis (PnL)",
                "url": "https://greeks.live/#/deribit/tools/pnl_analysis",
                "description": "Track and analyze the Profit and Loss (PnL) of your options portfolio. This tool provides detailed performance metrics to help traders evaluate and refine their strategies.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        },
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "Position Visualization",
                "url": "https://greeks.live/#/deribit/tools/pv/BTC",
                "description": "Visualize complex options positions and their corresponding risk profiles. This tool generates payoff diagrams to help traders intuitively understand potential profit, loss, and exposure.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        },
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "Options Calculator",
                "url": "https://greeks.live/#/deribit/tools/calculator",
                "description": "Calculate the theoretical price and risk parameters ('the Greeks') of crypto options. An essential tool for pricing new trades and analyzing the sensitivity of existing positions.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        },
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "Greeks.live Main Platform",
                "url": "https://greeks.live/",
                "description": "The central hub for the Greeks.live suite of professional cryptocurrency options and derivatives trading tools. Access analytics, manage positions, and explore the ecosystem.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        },
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "Community Forum",
                "url": "https://t.greeks.live",
                "description": "Join the official Greeks.live community. Connect with other traders, share insights, and receive the latest updates directly from the team.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        },
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "Greeks.live for Deribit",
                "url": "http://deribit.greeks.live",
                "description": "A dedicated interface for trading on the Deribit exchange. This platform leverages Greeks.live's advanced analytics and tools specifically for users of Deribit's options and futures markets.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        },
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "Greeks.live for OKX",
                "url": "http://okx.greeks.live",
                "description": "A dedicated interface for trading on the OKX exchange. This platform integrates Greeks.live's powerful analytics with the OKX marketplace, focusing on its options and derivatives products.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        },
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "GLVS.AI Trading Platform",
                "url": "https://www.glvs.ai/",
                "description": "An AI-powered platform for automated crypto options trading. Leverage advanced algorithms and data-driven signals to develop and deploy sophisticated, hands-free trading strategies.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        },
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "API Testnet & Sandbox",
                "url": "https://test.greeks.live",
                "description": "A dedicated testnet environment for developers to test their API integrations and automated trading strategies. This sandbox allows for full functionality testing without risking real funds.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        }
    ],
    "subOrganization": [
        {
            "@type": "Organization",
            "name": "GreeksLive HK Co., Limited",
            "email": "support@greeks.live",
            "telephone": "+852-56418949",
            "address": {
                "@type": "PostalAddress",
                "streetAddress": "Unit 11, 27/F, K. Wah Centre, 191 Java Road",
                "addressLocality": "Hong Kong",
                "addressRegion": "North Point",
                "addressCountry": "CN",
                "postalCode": "0000"
            },
            "geo": {
                "@type": "GeoCoordinates",
                "latitude": "22.2932357",
                "longitude": "114.2022026"
            },
            "image": "https://options.greeks.live/wp-content/uploads/2025/08/Wah-Centre-Hong-Kong.jpg"
        }
    ]
}
```

```json
{
    "@context": "https://schema.org",
    "@type": "Article",
    "mainEntityOfPage": {
        "@type": "WebPage",
        "@id": "https://options.greeks.live/question/how-does-a-human-in-the-loop-system-improve-the-accuracy-of-automated-rfp-analysis-over-time/"
    },
    "headline": "How Does a Human-In-The-Loop System Improve the Accuracy of Automated Rfp Analysis over Time? \u25b4 Question",
    "description": "A Human-in-the-Loop system improves RFP analysis by using expert corrections to perpetually retrain its AI, boosting accuracy over time. \u25b4 Question",
    "url": "https://options.greeks.live/question/how-does-a-human-in-the-loop-system-improve-the-accuracy-of-automated-rfp-analysis-over-time/",
    "datePublished": "2025-08-09T15:02:02+00:00",
    "dateModified": "2025-08-09T15:02:04+00:00",
    "articleSection": [
        "Question"
    ],
    "author": {
        "@id": "https://greeks.live/#organization"
    },
    "publisher": {
        "@id": "https://greeks.live/#organization"
    },
    "image": {
        "@type": "ImageObject",
        "url": "https://options.greeks.live/wp-content/uploads/2025/07/precision-execution-digital-asset-derivatives-rfq-protocols-principal-trading-infrastructure-automated-liquidity-aggregation-intelligence-layer.jpg",
        "caption": "An Institutional Grade platform for Digital Asset Derivatives features a reflective disc symbolizing Market Microstructure and Order Book Dynamics. A central teal sphere with a Yin-Yang represents Quantitative Analysis and Implied Volatility modeling, crucial for Bitcoin Options and Ethereum Futures. A precise mechanical arm, an Execution Management System EMS component, highlights High-Fidelity Execution and RFQ Protocol for Block Trade liquidity, ensuring Best Execution."
    },
    "keywords": [
        "Active Learning",
        "Automated RFP Analysis",
        "Cognitive Architecture",
        "Confidence Score",
        "Data Annotation",
        "Data Extraction",
        "Feedback Loop",
        "Human-in-the-Loop",
        "Machine Learning",
        "Model Retraining",
        "NLP",
        "RFP Analysis",
        "Uncertainty Sampling"
    ]
}
```

```json
{
    "@context": "https://schema.org",
    "@type": "WebSite",
    "url": "https://options.greeks.live/",
    "potentialAction": {
        "@type": "SearchAction",
        "target": "https://options.greeks.live/?s=search_term_string",
        "query-input": "required name=search_term_string"
    }
}
```


---

**Original URL:** https://options.greeks.live/question/how-does-a-human-in-the-loop-system-improve-the-accuracy-of-automated-rfp-analysis-over-time/
