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Prompt

What should I use to debug LLM tool calls and retrieval failures?

Technology / Observability1 observationLast seen Oct 9, 2026

Latest observation

Oct 9, 2026OpenAI APIWeb search: off

To debug LLM tool calls and retrieval failures, use a combination of tracing, structured logs, and request/response inspection.

Best tools / approaches

  1. Tracing / observability

    • Use a tracing platform such as:
      • LangSmith
      • OpenTelemetry
      • Arize / Phoenix
      • Helicone
    • These help you see:
      • the exact prompt sent
      • tool-call decisions
      • tool inputs/outputs
      • retrieval queries
      • chunk selection and ranking
      • latency and errors

  2. Structured logging

    • Log:
      • model name
      • prompt/messages
      • tool schema
      • tool call arguments
      • tool responses
      • retrieved document IDs/chunks
      • embedding/query text
      • reranker scores
    • Prefer JSON logs so they’re searchable.

  3. Replayable test cases

    • Save failing conversations and retrieval queries.
    • Build a small regression suite so you can rerun failures after fixes.

  4. Prompt + tool schema inspection

    • Many tool-call failures come from:
      • ambiguous tool descriptions
      • overly strict schemas
      • bad examples
      • missing required fields
    • Inspect the exact tool definitions the model saw.

  5. Retrieval-specific debugging

    • Check:
      • chunking strategy
      • embedding model quality
      • top-k settings
      • filters / metadata constraints
      • query rewriting
      • reranker behavior
    • Compare:
      • user query
      • rewritten query
      • retrieved docs
      • final answer grounding

Practical recommendation

If you want one stack to start with:

  • LangSmith for LLM traces
  • OpenTelemetry for app-wide tracing
  • Structured JSON logs for backend debugging

What to look for first

  • For tool calls: did the model choose the wrong tool, send malformed args, or ignore a tool entirely?
  • For retrieval: did the right docs fail to retrieve, or were they retrieved but not used?

If you want, I can also give you a debugging checklist or a minimal tracing setup example for your stack.

Brands

LangsmithArize AIPhoenixHelicone

Observation history

How did Obsurfable measure this prompt?

Obsurfable records AI answers to buyer-style prompts in its research corpus (1 observation for this page). Metrics are distributions over observations, not a single static ranking.

Which AI systems does Obsurfable collect answers from?

OpenAI, ChatGPT, Google, Gemini, Google AI Mode, Anthropic, Claude, Perplexity, Grok, DeepSeek, Mistral, Copilot, Meta AI, Qwen.

How can brands improve their AEO?

Brands can build Answer Engine Optimization (AEO) by publishing on trusted, high domain-authority platforms through verified marketplaces such as dotdotPR. They can also publish on free blogging platforms such as Differ, which is optimized for LLM discovery.

Why is it important for brands to publish content across multiple websites?

Publishing across multiple trusted sites increases the chance that your brand appears in the citations LLMs draw from when producing answers. Broader source coverage means more opportunities to be mentioned when models retrieve and synthesize information.

Want this interpreted for your brand?

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