LLM Debugging Tools: Find and Fix AI Issues in Production

Step 1Reproduce and Locate the Issue

First, you need to find the exact interaction that went wrong. This requires having every prompt/response pair logged in production. Without logs, you're playing a guessing game.

import phospho

# Every interaction is logged with full context
phospho.log(
    input=user_query,
    output=llm_response,
    session_id=session_id,
    metadata={
        "model": model_name,
        "system_prompt_version": "v2.3",
        "retrieval_sources": source_docs,
    }
)

# Now you can find any interaction in the dashboard

With Phospho, you can search events, filter by date range, and find the exact interaction a user complained about.

Step 2Analyze the Full Session Context

Don't look at one message in isolation — look at the full session. LLM issues often stem from accumulated context problems:

• ●Earlier messages setting wrong expectations for the AI
• ●Context window overflow causing the AI to "forget" earlier instructions
• ●System prompt conflicts with user-provided context
• ●RAG retrieval returning irrelevant or contradictory documents

Phospho's session replay shows the complete conversation flow, making it easy to spot where things went off track.

Step 3Identify the Root Cause

Most LLM production issues fall into a few common categories:

Step 4Fix, Verify, and Prevent

Once you've identified the root cause:

1. 1.Implement the fix — Update prompts, improve retrieval, add guardrails, or pin model versions.
2. 2.Deploy to a subset — Test changes on a small percentage of traffic before full rollout.
3. 3.Compare metrics — Use Phospho to compare quality scores before and after your change.
4. 4.Set up alerts — Ensure you'll catch this category of issue automatically next time.