Understand the three parts of the task
Use three headings in a working brief: instructions, context, and output. Instructions describe the operation. Context provides the material needed for it. Output describes the shape of the expected result. Separating them makes it easier to locate the source of a problem when the response misses your goal.
Hugging Face’s text generation documentation describes generation from an initial prompt and explains that model specific input formatting can matter. Follow the selected tool’s current format requirements when turning a planning brief into an implementation.
Write an output contract you can inspect
Suppose you want a short directory description from an approved service record. Specify the audience, the facts that must be preserved, the requested length, and how missing information should be handled. An output contract can be plain language; it does not have to begin as a technical schema.
- State the task using one clear verb, such as summarize or classify.
- Supply the authoritative record and relevant definitions.
- Describe the required fields or sections in the response.
- Tell the workflow what to do with uncertain or absent information.
- List checks for factual consistency and usable formatting.
For tasks that return structured records to another service, consult the AI API planning guide before connecting the result to an action.
Keep context relevant and traceable
Include the information needed for the current decision. Label examples clearly so a sample value is not mistaken for a fact about the real record. If you supply several documents, preserve their identities and explain which one should control when they disagree.
Keep source material separate from operational instructions in your application design. A document being summarized should remain evidence to examine. Decide how the workflow handles conflicting instructions inside that material before allowing generated output to affect another system.
Try an incomplete example before expanding the workflow. If a service record has no opening hours, specify that the output should preserve the gap or request review. Then inspect whether the result follows that rule. The example tests how your instructions handle uncertainty in practice.
Common mistake: treating fluent wording as verification
A readable summary still needs comparison with the original record. Check names, dates, qualifiers, and any sentence that introduces a new claim. For classification, include examples that do not fit any available category and define the desired outcome for them.
Record the prompt and context alongside the reviewed output. The AI LLM evaluation guide explains how to compare revisions and keep task quality visible over time.
Choose the environment after defining the task
The same brief can inform an evaluation of different model environments. Review the local model planning guide if hardware and deployment location matter. Then use the local versus frontier model comparison to structure a task based evaluation rather than choosing solely by a model label.
