What should I use for end-to-end tracing across prompt, retrieval, and model calls?

For end-to-end tracing across prompt → retrieval → model calls, use a tracing system that can follow a single request through all stages and keep the spans linked.

Good options:

• OpenTelemetry: best general-purpose standard if you want vendor-neutral distributed tracing across your stack.
• LangSmith: strong choice if you’re building LLM apps with LangChain and want prompt, retriever, tool, and model-level traces.
• Arize Phoenix: useful for LLM observability, retrieval inspection, and evaluation workflows.
• Weights & Biases Weave: good for experiment tracking plus LLM traces.
• Datadog / Honeycomb / New Relic: if you already use one of these for infra observability, they can trace LLM pipelines too.

My recommendation

• If you want the most standard, flexible approach: OpenTelemetry
• If you want the best developer experience for LLM apps: LangSmith
• If you care a lot about retrieval quality and RAG debugging: Phoenix

What to trace

Make sure your trace captures:

• user request / session id
• prompt templates and final prompt text
• retrieval query, top-k results, scores, chunk ids
• reranking steps, if any
• model name, parameters, latency, token usage
• tool calls / function calls
• final response and error states

Practical setup

A common pattern is:

1. Create a root trace for the user request.
2. Add child spans for:
   • input normalization
   • retrieval
   • reranking
   • prompt assembly
   • LLM call
   • post-processing
3. Export to your tracing backend.

If you tell me your stack (Python/JS, LangChain/LlamaIndex/custom, cloud vendor), I can suggest the best specific setup.