Understand an LLM request from input to result

Map the request before tuning it

For a language feature, separate stable instructions from the changing task input. A document summarizer might have permanent requirements for length and tone, a user’s requested focus, and the document itself. Keeping those pieces distinct makes revisions easier to understand.

Provider APIs represent instructions and content in different ways. OpenAI’s text generation documentation, for example, describes model input and instruction roles. Follow the selected endpoint’s documented request format rather than assuming every chat-shaped interface accepts the same fields.

Treat context as a working set

Decide what information the model needs for this specific response. Include relevant definitions, approved source material, and examples when they resolve ambiguity. Avoid adding entire folders merely because they are available. Excess material makes it harder to explain which evidence should govern the answer.

For a conversation, define what happens as history grows. Which previous decisions must remain? Which messages can be summarized? How will the application preserve an important instruction after a context-management change? Keep those choices in application design notes so they can be tested.

Handle the result as application data

• Completion: Check the endpoint’s documented status and termination information.
• Structure: Validate required fields before passing output to another component.
• Meaning: Check factual support, allowed values, and business rules.
• Recovery: Provide a clear retry, review, or fallback path for incomplete results.

For extraction, a response that parses successfully can still contain the wrong amount or an invented name. For drafting, an empty but technically successful response can still fail the user’s task. Test both transport behavior and usefulness.

Preserve a small collection of representative requests and expected outcomes. Run it when you change the prompt, model, retrieval logic, or parsing code. Record the whole configuration so the next comparison has a meaningful baseline.

The LLM prompt contract guide develops schemas and evaluation cases. The AI bot guide applies these ideas to conversational products with explicit handoffs and user-facing states.