User Request ↓ Context Retrieval ↓ Prompt ↓ LLM ↓ Generated Code ↓ Validation
One of the simplest things an AI builder can do is create a small evaluation dataset.
Now you can run your AI system against the same cases whenever you change: The prompt The model The retrieval system The context The workflow
I discussed the importance of this broader discipline in The Real Reason Prompt Engineering Isn't Going Away.
Prompt v1 ↓ Evaluation ↓ Results ↓ Prompt v2 ↓ Evaluation ↓ Compare
You can have a perfect prompt and still get a terrible answer because the AI received the wrong context.
The model may generate perfectly reasonable code based on completely incorrect information.
This is why I believe context engineering is becoming just as important as prompt engineering.
I wrote about this in Why Context Engineering Is More Important Than Prompt Engineering.
Over time, your evaluation dataset becomes one of the most valuable assets in your AI project.
I've started thinking about AI systems in three layers: Can it produce an answer?
The AI industry has spent enormous effort improving: Models Prompts Agents RAG MCP Vector databases
I'm also adding a simple AI evaluation starter to my companion AI Builder resources.
ai-evaluation/ ├── README.md ├── test_cases.json ├── evaluate.py ├── results.csv └── prompts/ ├── v1.txt └── v2.txt
The future of AI engineering won't belong only to people who know how to make models produce impressive outputs.
It will belong to developers who can measure, reproduce, debug, and improve those outputs.