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.