# AI Workflow: Why Structured Evaluation Pipelines Matter > Source: > Published: 2026-07-23 20:06:57+00:00 # AI Workflow: Why Structured Evaluation Pipelines Matter The core problem is that LLMs are non-deterministic. A tweak that fixes a bug in one edge case often breaks three other things you weren't looking at. To stop this cycle, you need a system that treats prompts like code—complete with unit tests and regression suites. ## Building a Basic Eval Framework If you're starting from scratch, don't overcomplicate it. You just need three things: a golden dataset (input/expected output pairs), a set of heuristics or LLM-as-a-judge metrics, and a way to run them in batch. 1. **The Golden Set:** Curate 50-100 high-signal examples. These should include the "happy path" and the weirdest edge cases that have previously caused failures. 2. **Evaluation Metrics:** - **Deterministic:** Exact match, regex checks, or JSON schema validation. - **Semantic:** Using an LLM (like [Claude](/en/tags/claude/) or GPT-4o) to grade the response based on a specific rubric (Accuracy, Tone, Conciseness). 3. **The Pipeline:** Run your new prompt against the entire set and calculate a pass rate. ## The "LLM-as-a-Judge" Pitfall One thing I've noticed is that using a model to grade another model can be deceptive. If your judge model has the same biases as your generator, it'll give you a 100% score while the actual output is garbage. To fix this, your evaluation prompt needs to be incredibly strict. Instead of asking "Is this good?", ask "Does this output contain [X] and avoid [Y]? Answer only YES or NO." This approach turns a subjective "feeling" into a hard metric. When you can see a dashboard showing that a prompt change increased accuracy from 72% to 85% across 200 samples, you can actually deploy with confidence. [Next iPhone 15 Giveaway: Conversion Analysis →](/en/threads/2510/)