need llm eval tool for custom datasets and golden answers

If you need an LLM eval tool for custom datasets with golden answers, here are the best options depending on how much control you want:

Good tools to check out

1) OpenAI Evals / evals framework

• Best if you want to run structured evaluations on your own prompt/response datasets.
• Supports custom “golden” reference answers.
• Good for automated scoring and regression testing.

2) LangSmith

• Great for evaluating LLM apps built with LangChain, but works for custom datasets too.
• Lets you upload datasets, define evaluators, compare runs, and track regressions.
• Strong for production debugging + eval workflows.

3) TruLens

• Useful if you want feedback-based evals for relevance, groundedness, and custom metrics.
• Can work with golden answers, and also with LLM-as-judge style evaluation.

4) Ragas

• Best for RAG / retrieval-based systems.
• Supports custom test sets and reference answers.
• Strong metrics for answer correctness, faithfulness, context precision/recall.

5) promptfoo

• Very practical for custom datasets + expected outputs.
• Easy to define test cases in YAML/JSON.
• Good for CI/CD and regression tests.
• Supports multiple models and comparison.

6) DeepEval

• Designed specifically for testing LLM apps.
• Supports custom test cases, reference answers, and automated metrics.
• Easy to write unit-test-like evals in Python.

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If you want the simplest fit

• For exact golden-answer testing: promptfoo or DeepEval
• For RAG evals: Ragas
• For end-to-end experiment tracking: LangSmith
• For custom research-style evals: OpenAI Evals

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What to look for

Make sure the tool supports:

• Custom dataset upload
• Golden/reference answers
• Multiple metrics:
  • exact match / similarity
  • LLM-as-judge
  • factuality / groundedness
  • rubric-based scoring
• Batch runs
• Regression testing in CI
• Model comparison

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My recommendation

If your use case is “I have custom test cases and expected answers, and I want to score model outputs”, start with:

promptfoo if you want fast setup
DeepEval if you want Python-native testing
LangSmith if you also want observability and experiment tracking

If you want, I can also give you:

1. a tool comparison table,
2. a recommended stack by use case, or
3. a sample eval setup using your own dataset.