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title: 'Part 8: Building Feedback Loops with Ragas'
date: 2025-05-04T06:00:00.000Z
layout: blog
description: >-
  A research-driven guide to designing robust, actionable feedback loops for LLM
  and RAG systems using Ragas. Learn how to select metrics, set baselines,
  define thresholds, and incorporate user and human feedback for continuous
  improvement.
categories:
  - AI
  - RAG
  - Evaluation
  - Ragas
  - Data
coverImage: /images/building-feedback-loops.png
readingTime: 10
published: true

A high-performing LLM or RAG system is never static. The most successful teams treat evaluation as a continuous, iterative process—one that closes the loop between measurement, analysis, and improvement. In this post, we’ll design a research-backed feedback loop process using Ragas, focusing on actionable activities at each stage and strategies for integrating user and human feedback.

Designing the Feedback Loop: A Stepwise Process

The feedback loop process is a systematic approach to continuously improve your LLM or RAG system. It consists of seven key steps, each building on the previous one to create a sustainable cycle of evidence-driven progress.

Feedback Loop Process

1. Select the Right Metric

Purpose:
Identify metrics that best reflect your application’s goals and user needs.

Activities:

  • Map business objectives to measurable outcomes (e.g., accuracy, faithfulness, relevancy).
  • Review available Ragas metrics and select those most aligned with your use case.
  • Periodically revisit metric selection as your product or user base evolves.

2. Develop and Measure Baseline Metrics

Purpose:
Establish a reference point for current system performance.

Activities:

  • Assemble a representative evaluation dataset.
  • Run your system and record metric scores for each example.
  • Document baseline results for all selected metrics.
  • Ensure the baseline dataset remains stable for future comparisons.

3. Analyze and Define Acceptable Threshold Values

Purpose:
Set clear, actionable standards for what constitutes “good enough” performance.

Activities:

  • Analyze baseline metric distributions (mean, variance, outliers).
  • Consult stakeholders to define minimum acceptable values for each metric.
  • Document thresholds and rationale for transparency.
  • Consider different thresholds for different segments (e.g., critical vs. non-critical queries).

4. Evaluate and Select Improvement Areas

Purpose:
Identify where your system most often fails to meet thresholds and prioritize improvements.

Activities:

  • Segment evaluation results by metric, query type, or user group.
  • Identify patterns or clusters of failure (e.g., certain topics, long queries).
  • Prioritize areas with the greatest impact on user experience or business goals.
  • Formulate hypotheses about root causes.

5. Implement Improvements

Purpose:
Take targeted actions to address identified weaknesses.

Activities:

  • Design and implement changes (e.g., prompt tuning, retrieval upgrades, model fine-tuning).
  • Document all interventions and their intended effects.
  • Ensure changes are isolated for clear attribution of impact.

6. Record Metrics for History

Purpose:
Build a longitudinal record to track progress and avoid regressions.

Activities:

  • After each improvement, re-evaluate on the same baseline dataset.
  • Log metric scores, system version, date, and description of changes.
  • Visualize trends over time to inform future decisions.

Metric Record Log Schema Example:

Timestamp System Version Metric Name Value Dataset Name Change Description
2025-05-04T12:00:00 v1.2.0 faithfulness 0.78 baseline_v1 Added re-ranking to retriever
2025-05-04T12:00:00 v1.2.0 answer_relevancy 0.81 baseline_v1 Added re-ranking to retriever
... ... ... ... ... ...

7. Repeat: Analyze, Evaluate, Implement, Record

Purpose:
Establish a sustainable, iterative cycle of improvement.

Activities:

  • Regularly revisit analysis as new data or feedback emerges.
  • Continuously refine thresholds and priorities.
  • Maintain a culture of evidence-based iteration.

Integrating User Feedback in Production

Purpose

User feedback provides real-world validation and uncovers blind spots in automated metrics. Incorporating it closes the gap between technical evaluation and actual user satisfaction.

Strategies

  • In-Product Feedback Widgets: Allow users to rate answers or flag issues directly in the interface.
  • Passive Signals: Analyze user behavior (e.g., follow-up queries, abandonment) as implicit feedback.
  • Feedback Sampling: Periodically sample user sessions for manual review.
  • Feedback Aggregation: Aggregate and categorize feedback to identify recurring pain points.
  • Metric Correlation: Analyze how user feedback correlates with automated metrics to calibrate thresholds.

Recording User Feedback

User Feedback Log Schema Example:

Timestamp User ID Query ID User Rating Feedback Text Metric Scores System Version
2025-05-04T13:00:00 12345 q_987 2 "Answer was off-topic" {faithfulness: 0.6, answer_relevancy: 0.5} v1.2.0
2025-05-04T13:00:00 67890 q_654 4 "Good answer, but could be more concise" {faithfulness: 0.8, answer_relevancy: 0.9} v1.2.0
... ... ... ... ... ... ...

Including Human Labelers in Evaluation

Purpose

Human labelers provide high-quality, nuanced judgments that automated metrics may miss, especially for ambiguous or complex queries.

Strategies

  • Periodic Human Review: Regularly sample evaluation outputs for human annotation.
  • Disagreement Analysis: Focus human review on cases where user feedback and metrics disagree.
  • Labeler Training: Provide clear guidelines and calibration sessions to ensure consistency.
  • Hybrid Scoring: Combine human and automated scores for a more holistic evaluation.
  • Continuous Calibration: Use human labels to refine and validate automated metric thresholds.

Conclusion

A robust feedback loop is the foundation of sustainable improvement for LLM and RAG systems. By systematically selecting metrics, measuring baselines, setting thresholds, and integrating both user and human feedback, you create a virtuous cycle of evidence-driven progress. The most effective teams treat evaluation as an ongoing process—one that is deeply connected to real user outcomes and grounded in transparent, repeatable measurement.

This is the eighth part of a series on Ragas, a research-driven evaluation framework for LLM and RAG systems. If you missed the previous parts, check them out below:

Part 1: Introduction to Ragas: The Essential Evaluation Framework for LLM Applications
Part 2: Basic Evaluation Workflow
Part 3: Evaluating RAG Systems with Ragas
Part 4: Test Data Generation
Part 5: Advanced Metrics and Customization
Part 6: Evaluating AI Agents
Part 7: Integrations and Observability
Part 8: Building Feedback Loops — You are here

Have questions or want to share your feedback loop strategies? Connect with me on LinkedIn for discussion or collaboration!