A hands-on workshop for engineers moving from ChatGPT-style prompting to production-grade prompt engineering.
Topics Covered
- Structured outputs — JSON mode, function calling, and Pydantic validation
- Prompt versioning — treating prompts as code with version control and A/B testing
- Guardrails — input validation, output filtering, and hallucination detection
- Testing prompts — unit tests for LLM outputs using semantic similarity and rubric grading
- Cost optimization — prompt caching, token budgeting, and model routing
Key Takeaway
The gap between a demo prompt and a production prompt is the same as the gap between a script and a service — error handling, testing, monitoring, and versioning.