feedback-loops

Feedback Loops

Purpose: Build systems that continuously improve AI/ML models through structured human and automated feedback mechanisms.

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When to Use This Skill

• Collecting and integrating user feedback into model improvement
• Implementing RLHF (Reinforcement Learning from Human Feedback) pipelines
• Designing reward models for preference-based training
• Building automated feedback systems (RLAIF - AI feedback)
• Creating annotation pipelines for training data refinement
• Establishing continuous improvement cycles for production AI systems

Prerequisites

• Deployed AI system with feedback collection capability
• Storage for feedback data (structured database)
• Annotation pipeline or LLM-as-judge for automated feedback
• Retraining pipeline (connects to Model Fine-Tuning skill)

Decision Tree

Setting up feedback?
+- What type of feedback?
|  +- User signals (thumbs up/down, ratings)?
|     -> Implicit feedback collection
|  +- User corrections (edited responses)?
|     -> Explicit feedback with preference pairs
|  +- Expert annotations (labeled data)?
|     -> Annotation pipeline
|  +- Automated (LLM-as-judge)?
|     -> RLAIF pipeline
+- What is the improvement goal?
|  +- Alignment (safety, helpfulness)?
|     -> RLHF / DPO with preference data
|  +- Accuracy (factual correctness)?
|     -> Curated fine-tuning data from corrections
|  +- Style / format?
|     -> Supervised fine-tuning on preferred outputs
|  +- Coverage (new topics)?
|     -> RAG index expansion from feedback gaps
+- How often to improve?
   +- Continuous (online learning)? -> Real-time feedback pipeline
   +- Periodic (batch retraining)? -> Scheduled feedback aggregation
   +- On-demand (triggered)? -> Threshold-based retraining

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Feedback Loop Architecture

Full-Cycle Pipeline

[Users / Operators]
       |
       v
[AI System (Inference)]
       |
       v
[Response + Feedback UI]
       |
       v
[Feedback Collection]
       |
       +-- [Structured Storage (DB)]
       |        |
       |        v
       |   [Feedback Aggregation]
       |        |
       |        v
       |   [Quality Filter]
       |        |
       |        v
       |   [Training Data Builder]
       |        |
       |        v
       |   [Fine-Tuning / RLHF Pipeline]
       |        |
       |        v
       |   [Evaluation Gate]
       |        |
       |        v
       |   [Model Registry]
       |
       +-- [Analytics Dashboard]
       |        |
       |        v
       |   [Insights: Failure Patterns, Gaps, Trends]
       |
       +-- [RAG Index Update] (if feedback reveals knowledge gaps)