RLHF and Human Feedback

Continuous alignment from user sentiment.

Overview

Reinforcement Learning from Human Feedback (RLHF) allows users to grade generation quality. Arivu natively captures thumb-ups/downs and descriptive feedback via chat integrations to shape prompt optimizations.

Intent Rectification

Locate queries where the LLM misunderstood user nuance and track corrections over time.

Approval Queues

Destructive queries (UPDATE/DELETE) are parked for admin consent before execution.

How It Works

1. Signal Capture

   Users provide feedback (positive/negative) via the chat UI, Telegram, or any integration adapter. The signal is attached to the query’s trace record.

2. Storage

   RLHF signals persist in the memory backend (SQLite or Redis), indexed by session_id and timestamp.

3. Analysis

   The dashboard aggregates feedback trends, helping you identify which types of queries consistently generate poor results.

The rlhf_feedback LangGraph node executes entirely downstream of the response_generator. Injecting RLHF signals adds zero blocking latency to your conversational UX.