What should I use instead of just logs for LLM app debugging?

For LLM app debugging, plain logs are usually not enough. You typically want a mix of traces, structured events, prompt/version tracking, evaluations, and replayable records.

Here’s what to use instead of just logs:

1) Tracing

Track each user request as a span/tree of steps:

• input received
• retrieval calls
• tool/function calls
• model prompts
• model outputs
• post-processing
• final response

This helps you see where latency, errors, or bad outputs originate.

2) Structured event logging

Instead of free-form text logs, record JSON-like events with fields such as:

• request_id / trace_id
• user_id / session_id
• model name + version
• prompt template version
• retrieved docs
• tool inputs/outputs
• token counts
• latency
• error codes

This makes debugging and filtering much easier.

3) Prompt/version tracking

Store:

• prompt template versions
• system prompt versions
• tool schema versions
• retrieval configuration
• model parameters

A lot of “bugs” are really prompt/config regressions, not code bugs.

4) Input/output capture with redaction

Save representative:

• user input
• model output
• tool output
• retrieved context

But redact sensitive data. This is critical for reproducing bad behavior.

5) Evaluation harnesses

Use automated evals for:

• correctness
• faithfulness / hallucination
• relevance
• refusal quality
• tool-use accuracy
• formatting compliance

This is the best way to catch regressions before production.

6) Replay / time travel debugging

Be able to rerun a request with:

• the original prompt
• same retrieved docs
• same tool outputs
• same model version if possible

This helps reproduce issues reliably.

7) Observability metrics

Track high-level metrics like:

• success/failure rate
• tool error rate
• retrieval hit rate
• hallucination rate
• latency percentiles
• token usage / cost
• user escalation / thumbs down

8) Human review / conversation inspection

For tricky issues, inspect:

• full conversation history
• intermediate chain-of-thought equivalent artifacts only if your system intentionally stores safe reasoning traces; otherwise avoid storing private internal reasoning
• the exact context the model saw

Best practical setup

If you want a simple recommendation:

• tracing for execution flow
• structured logs for searchable data
• prompt/version registry for reproducibility
• evals for regression testing
• replay tooling for debugging

Tools people often use

Depending on your stack:

• OpenTelemetry for traces/metrics
• LangSmith, Helicone, PromptLayer, Weights & Biases, Arize/Phoenix for LLM observability
• custom JSON event storage + dashboard if you want full control

If you want, I can also suggest a minimal debugging architecture for an LLM app or a schema for tracing LLM requests.