Weights & Biases Weave for LLM evaluation

Weights & Biases Weave is a framework for instrumenting, evaluating, and debugging LLM applications. It’s especially useful when you want to go beyond prompt experimentation and actually measure how your app behaves across many inputs, model versions, and traces.

What Weave is good for

• Tracing LLM calls and app workflows end-to-end
• Logging inputs, outputs, and intermediate steps
• Evaluating prompt/model changes with test datasets
• Comparing runs across versions
• Inspecting failures in a UI, not just in raw logs

Typical LLM eval workflow with Weave

1. Instrument your app
   • Wrap functions or LLM calls so Weave can capture traces.
2. Create a dataset
   • Use a set of representative prompts, tasks, or labeled examples.
3. Define evaluation metrics
   • Could be:
     • exact match / regex / format checks
     • LLM-as-judge scores
     • custom Python scoring functions
     • human-reviewed labels
4. Run evaluations
   • Execute your app over the dataset.
5. Review results
   • Inspect per-example outputs, errors, and aggregate metrics.
6. Iterate
   • Improve prompts, tools, retrieval, or model choice and re-run.

Example use cases

• Chatbot quality
  • Measure helpfulness, correctness, and tone
• RAG systems
  • Check answer faithfulness to retrieved context
• Extraction tasks
  • Validate JSON schema compliance
• Tool-using agents
  • Evaluate whether the agent took the right actions
• Safety evaluations
  • Detect policy violations or hallucinations

Simple Python sketch

import weave

weave.init("my-llm-project")

@weave.op()
def answer પ્રશ્ન(prompt: str):
    # call your model here
    return {"response": "..."}

# later: run over a test set and score results

Strengths

• Tight integration with W&B ecosystem
• Good support for trace visualization
• Makes it easier to compare prompt/model iterations
• Useful for production debugging and offline evals

If you want to use it well

A solid eval setup usually includes:

• a small golden set of hand-labeled examples
• a larger fuzz/test set
• at least one deterministic metric
• optionally an LLM judge for subjective dimensions
• a way to track versions of prompts and models

If you want, I can also give you:

1. a minimal Weave eval example,
2. a RAG evaluation setup, or
3. a comparison between Weave, LangSmith, and Helicone.