Which of the following are components of a production-grade prompt beyond 'just instructions'?

A production prompt is a structured document, not a one-line instruction. It usually contains a directive about what to do, a directive about the output format, a few-shot block with examples, a refusal rule for out of scope inputs, a grounding constraint that tells the model where to draw facts from, and a layout that places retrieved documents and the user query in known positions. All of those things are text inside the prompt string sent to the model. The model reads them as one big input and generates an output that respects them. Temperature and top-p are different. They are API call parameters set next to the prompt, not inside it. They control how the model picks the next token from its probability distribution once it has read the prompt. Higher temperature makes sampling more random; lower temperature makes it more deterministic. The distinction matters in practice. Prompt edits live in version control, get reviewed, and get evaluated. Decoding parameters can be changed in a config file and ship with much less ceremony. Folding them together usually means decoding settings get changed casually, which causes regressions that are hard to trace.

Production prompts have anatomy. Beyond the core instructions, a well-designed prompt includes several distinct components. An output-format directive pins the response shape (JSON schema, length budget, structured wrapper). Few-shot examples curated for diversity, a refusal directive for out of scope inputs, and a grounding constraint that restricts the model's source of facts each get their own block. A context layout fixes where retrieved documents and the user query appear. All of those are authored content inside the prompt string. They are part of what the model reads and they shape how it interprets and responds. Temperature and top-p sit outside the prompt. They are decoding parameters, set in the API call alongside the prompt. They control sampling from the next-token distribution after the model has finished processing the input. They influence output randomness and diversity but they are not part of the prompt the model attends over. The distinction is more than vocabulary. Prompts get versioned, reviewed, and evaluated; decoding parameters often get changed in a config file and ship with much less ceremony. Treating decoding as 'part of the prompt' encourages casual changes that introduce regressions and bypass the prompt evaluation pipeline. Production-grade hygiene is to track them as separate artifacts with separate ownership and separate eval gates.