> ## Documentation Index > Fetch the complete documentation index at: https://docs.openlit.io/llms.txt > Use this file to discover all available pages before exploring further. # Manual Feedback > Add human feedback - good, bad, or neutral, with an optional comment - directly on a trace Not every quality signal needs an LLM judge. Manual Feedback lets a human rate a specific trace directly, alongside the automated evaluations already running on it. ## How it works 1. Open any LLM trace in [Telemetry](/latest/openlit/observability/telemetry/traces) and click its **Evaluation** tab. 2. In the **Feedback** panel, rate the response **Good**, **Bad**, or **Neutral**. 3. Optionally add a comment explaining the rating. 4. Click **Submit Feedback**. Your feedback is stored as its own evaluation entry (`manual_feedback`) in the same evaluation pipeline as the automated evaluators - so it appears in the trace's evaluation list right alongside Hallucination, Bias, and any other evaluator results, and rolls up into [Evaluations → Analytics](/latest/openlit/evaluations/analytics). Score follows the same convention as the automated evaluators (low score = no issue): **Good** stores `0`, **Neutral** stores `0.5`, and **Bad** stores `1`. Your comment is saved as the entry's explanation. ## When to use it * Spot-checking responses an automated evaluator didn't flag, or disagreeing with one that did * Collecting reviewer or subject-matter-expert sign-off on specific traces * Capturing qualitative notes that don't fit a built-in or custom evaluation type *** Built-in and custom evaluation types Automated scoring for hallucination, bias, toxicity, and more Auto Evaluation schedule and judge model