langsmith-online-eval-engineering

これはレビュー時に保存された抜粋です。完全で最新の内容は外部ソースを確認してください。

---
name: langsmith-online-eval-engineering
description: Iteratively inspect traces, interview the user, and create LangSmith online evaluators one at a time. Use specifically for creating online evaluators for use within LangSmith -- use "eval-engineering" for Harbor-style online evaluations. 
---

# Online Eval Engineering

Build online evaluators iteratively:

```text
inspect traces and interview user -> propose directions -> user chooses
-> build evaluator -> test, attach, verify -> review and repeat
```

Read [references/langsmith-api.md](references/langsmith-api.md) before creating or modifying evaluators.

## 1. Inspect traces

Ask the user for their LangSmith project name. Fetch recent root-level traces and print their structure. Read [references/trace-inspection.md](references/trace-inspection.md). Find:

- run name and type;
- available input and output field names;
- the shape and content of the data (truncated samples);
- which fields carry the data an evaluator would need.

Summarize the trace structure in the conversation:

```text
Project: name
Run type: chain | llm | tool | ...
Input fields: field names and what they contain
Output fields: field names and what they contain
Sample: one representative input/output pair (truncated)
```

Keep the user involved: explain the trace structure and what it implies, then ask only for information the traces cannot establish. For example: "What does this application do?", "What quality concern matters most?", or "What failure should never happen?"

Ask whether the user wants a naming prefix for evaluators in this session (e.g., `myapp-`, `v2-`, `dogfood-`). If they provide one, apply it to all evaluator names, prompt hub handles, and run rule display names. If they decline, use plain descriptive names.

Do not propose evaluators until the trace structure is understood and the user has described their concerns.

## 2. Discuss and choose an eval direction

Read [references/evaluator-design.md](references/evaluator-design.md). Propose two or three evaluation criteria grounded in the trace data. Apply the naming prefix from step 1 if the user provided one. For each, give:

```text
Name: descriptive evaluator name (with prefix if set)
Type: LLM-as-judge or code
Measures: what quality dimension this evaluates
Scoring: bool, float (0-1), or int; what pass/fail means
Fields needed: which trace fields are used and how
Rationale: why this type and approach
```

Example:

```text
Name: response-relevance
Type: LLM-as-judge
Measures: whether the response addresses the user's question
Scoring: bool; True = relevant, False = off-topic or non-responsive
Fields needed: input (user question), output (assistant response)
Rationale: relevance is semantic and requires reading comprehension; not decidable by code
```

Recommend one and ask the user which to build. Do not implement until the user chooses.

## 3. Build one evaluator

Read [references/langsmith-api.md](references/langsmith-api.md). Build the selected evaluator. Show the full configuration to the user and get approval before executing any API calls.

**LLM-as-judge path.** Define a `ResponseSchema` with `reasoning` first, then the score field. Write prompt messages with a clear rubric that assesses the result, not whether it matches a reference answer. Set `variable_mapping` using field names discovered in step 1. Present the schema, prompt, variable mapping, and evaluator name for approval. On approval, push the prompt and create the evaluator. Report the evaluator ID.

**Code evaluator path.** Write a `perform_eval(run, example=None)` function. It must be self-contained (only builtins and standard library), access `run` as a dict (`run.get("outputs")`), and return `{"key": ..., "score": ..., "comment": ...}`. Present the function code and evaluator name for approval. On approval, create the evaluator. Report the evaluator ID.

## 4. Test, attach, and verify

Before attaching, ask the user what sampling rate they want (1.0 = every