> ## Documentation Index
> Fetch the complete documentation index at: https://arivu.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# RLHF and Human Feedback

> Continuous alignment from user sentiment.

<Frame>
  <img src="https://mintcdn.com/arivu/piekGGEpY74vFGfF/images/arivu-feedback.png?fit=max&auto=format&n=piekGGEpY74vFGfF&q=85&s=a007a1e65684face38721a828978b3c5" alt="RLHF Feedback" className="rounded-xl border border-gray-200 dark:border-gray-800" width="1919" height="912" data-path="images/arivu-feedback.png" />
</Frame>

## 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.

<CardGroup cols={2}>
  <Card title="Intent Rectification" icon="compass">
    Locate queries where the LLM misunderstood user nuance and track corrections over time.
  </Card>

  <Card title="Approval Queues" icon="check-double">
    Destructive queries (UPDATE/DELETE) are parked for admin consent before execution.
  </Card>
</CardGroup>

## How It Works

<Steps>
  <Step title="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.
  </Step>

  <Step title="Storage">
    RLHF signals persist in the memory backend (SQLite or Redis), indexed by `session_id` and `timestamp`.
  </Step>

  <Step title="Analysis">
    The dashboard aggregates feedback trends, helping you identify which types of queries consistently generate poor results.
  </Step>
</Steps>

<Info>
  The `rlhf_feedback` LangGraph node executes entirely downstream of the `response_generator`. Injecting RLHF signals adds **zero blocking latency** to your conversational UX.
</Info>