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

Multi-select·Medium·4.0 · 0·~1 min·Asked atJane Street LangChain LlamaIndex·Relevant atAnthropic OpenAI

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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.

TL;DR

A production prompt is more than rules: format directive, few-shot, refusal, grounding, and layout all live inside the string; temperature and top-p sit outside it.

Memory aid

Easy to grasp

Imagine writing a brief for a freelance designer. You include the goal, examples of what you like, the file format you want, what to do if the request gets weird, and where they should pull source images from. All of that is the brief. The brief is not the same as how fast they type or how many revisions they iterate through. Those are separate dials in their workflow. Production prompts work the same way. The prompt is the brief you send to the model: directives, examples, format, grounding rules, layout. Sampling settings like temperature are not part of the brief; they control how the model picks its next word once it has read the brief.

Key concepts

Decoding Parameters Prompt Anatomy

Concept explanation~2 min read

Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example. Click to expand.

Prompt anatomy is one of those subjects where the vocabulary matters. A production prompt is not a single instruction; it is a structured document with several components, each of which has its own design rules and failure modes. A useful first step in any prompt review is to enumerate the components present, name the ones missing, and ask whether anything in the request is being conflated with decoding parameters or tool definitions.

The goal of this deep dive is to lay out the canonical prompt components, explain why decoding parameters belong outside the prompt, and give a senior framing for the three-surface review pattern (prompt, decoding, tools). The interview-worthy framing is to name the components, draw the line between prompt and decoding cleanly, and explain why the line matters for eval and rollout discipline.

The sections below walk through the six prompt components, the role of decoding parameters, the registry discipline that respects the distinction, and the per-surface failure modes a senior engineer should be able to identify from a prompt audit.

The six components of a production prompt

A production prompt typically has six things in it beyond raw instructions.

The behavioral directives describe what the model should do (persona, scope, decision logic). The output-format directive pins the response shape: a JSON schema, a structured XML wrapper, a length budget, or an explicit formatting template. The few-shot examples block carries one to five examples curated for diversity, each one paired input-output. The refusal directive tells the model what to do when the input is out of scope or violates policy. The grounding constraint restricts the source of facts, often by telling the model to cite only from provided context and refuse to invent. The context layout fixes where retrieved documents, conversation history, tool outputs, and the current user query appear in the prompt.

All six are text inside the prompt string. The model reads them as one input and generates an output that respects them. A prompt that has fewer than three of these components usually has identifiable failure modes: shape drift if no format directive, hallucination if no grounding constraint, evasive answers if no refusal directive.

The order of components is itself a design choice. Stable, high-leverage content goes at the top of the system prompt; per-call content goes at the bottom. This ordering matters both for attention weighting and for prompt caching, where the cache fires on the longest byte-identical prefix.

Why decoding parameters sit outside the prompt

Registry discipline and the eval gate

Tool definitions as a third surface

Failure modes per surface

Situations where this technique stops working.

2–4 min · Everything important, quickly.

Real products, models, and research that use this idea.

Anthropic's Claude Opus 4.7 API distinguishes the prompt payload (system, messages, tool definitions) from decoding parameters like temperature on the request body, and the docs treat them as separate surfaces.
OpenAI's GPT-5.5 Responses API has explicit fields for response_format and tools alongside top-level temperature and top_p, reflecting the same separation in the SDK.

What an interviewer would ask next. Try answering before peeking at the approach.

QHow would you structure a prompt registry that respects the prompt vs decoding distinction?

Separate fields for prompt version, decoding params, model version, and tool schema; each independently pinned; per-call audit ties all four to a request id; rollout gated on per-surface evals.

2 more follow-ups an interviewer would ask next. Sign in to reveal them.

Red flags & common mistakes

The phrases that signal junior thinking. Click to expand.

Most common mistake

Folding temperature and top-p into 'the prompt' so they get changed without versioning, evals, or the diligence that prompt edits get.

60 second bullets to scan on the way to the call.

Six components inside the prompt: directives, format, few-shot, refusal, grounding, layout
Why temperature and top-p sit outside the prompt

Primary sources. Browse if you want the original framing.