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LLM Eval Framework Builder
You changed the prompt, tried four inputs, it looked better, you shipped — and three days later support tickets say outputs are worse for an entire class of inputs you didn't test
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See it in action
You say
We are building a customer support bot for a FinTech app using LangChain. I need to set up an evaluation framework to ensure it doesn't give bad financial advice and follows our brand voice.
Your agent does
Evaluation Framework for FinTech Support Bot
- Quality Dimensions: Financial Accuracy (Pass/Fail), Regulatory Compliance (Safety), and Brand Tone (1-5 Likert Scale).
- Golden Dataset: 50 pairs of real support tickets + 10 adversarial 'jailbreak' attempts.
- CI Integration: Block merges if 'Financial Accuracy' drops below 100% on the golden set.
What you get
About this skill
You changed the prompt, tried four inputs, it looked better, you shipped — and three days later support tickets say outputs are worse for an entire class of inputs you didn't test. Eval-less LLM development is just deferred debugging with a user-facing blast radius. This skill builds your evaluation framework: the test case set that covers your real distribution (not just the examples you had handy), the scoring rubric that maps to the quality dimensions that actually matter, the automated evaluation pipeline that runs on every prompt change, and the regression detection that tells you when a model upgrade quietly breaks your use case. It also covers the tricky parts — LLM-as-judge calibration, handling non-determinism in pass/fail metrics, and the human evaluation spot-checks that catch what automation misses. Give it your task, your current failure modes, and your quality bar; it returns an eval suite you can run in CI.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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