Prompt Engineering for Production AI
Prompt engineering is the disciplined design of instructions, context, examples, constraints, and output requirements supplied to a model.
A useful prompt structure
- Task — what the model must accomplish.
- Context — information required to perform the task.
- Constraints — boundaries, policies, and failure behavior.
- Output contract — required format and fields.
- Examples — representative inputs and outputs when they improve consistency.
Production practices
- Keep prompts versioned like code.
- Separate system instructions from untrusted user content.
- Require structured output where downstream software depends on a schema.
- Test adversarial and ambiguous inputs.
- Evaluate prompts against a fixed regression set before deployment.
- Measure quality, latency, token usage, and cost together.
Common mistake
A longer prompt is not automatically a better prompt. Extra instructions can increase cost, consume context, and create conflicting requirements.
Connected knowledge
LLMs → context-window troubleshooting → RAG → agentic AI?.