Prompt Engineer

Turn a vague request into a prompt that produces consistent, high-quality output on inputs you have not seen yet. The costly mistake this prevents is the adjective prompt: "be detailed and professional" reads like an instruction but constrains nothing, so output quality varies run to run and the author iterates blind. Structure and examples constrain; adjectives decorate.

Operating procedure

Ordering inside the prompt is load-bearing, not stylistic: instructions come before context so the model reads the data already knowing what to do with it, and the most important constraint is restated last because recency makes it stick.

Step 1: Gather inputs

1. The single objective, stated in one sentence. If it takes two sentences, it is two prompts - split into a chain.
2. 3-5 real example inputs, including one ugly one. Iterate on real inputs, not imagined ones; label synthetic examples as synthetic.
3. Who or what consumes the output: a downstream parser wants JSON with a schema; a human wants markdown.
4. The failure policy: what the model should do when input is missing, ambiguous, or out of scope (default: return "UNKNOWN" rather than guess).

Step 2: Build the four blocks, in this order

ROLE: You are a senior <domain> expert.
TASK: <imperative, one goal - placed before the context it operates on>
FORMAT: Respond as <literal JSON schema / markdown table / bullets>.
RULES:
- If <field> is missing, return "UNKNOWN" rather than guessing.
- Keep reasoning internal; output only the final result.
CONTEXT:
<facts, constraints, audience - and any user-supplied content, delimited>
<restate the single most important constraint here, last>

Instructions before context: a model that reads the task first processes the context purposefully; a model that reads three pages of context first has already formed its own idea of the job. Pin the output format with a literal schema or example, never a description of one.

Step 3: Replace adjectives with examples

Examples beat adjectives every time. Add 1-3 few-shot examples whenever the output shape is non-obvious, choosing examples that mark the decision boundaries - the near-misses, not the easy cases.

Bad - adjectives, no shape, no failure policy:

Analyze this customer review and tell me the sentiment. Be accurate,
thorough, and professional. Don't get it wrong.

Review: "Shipping was fast but the box was crushed."

Why it fails: "accurate, thorough, professional" constrains nothing measurable; the output shape is unpinned so one run returns a paragraph and the next a single word; no labels are enumerated, so the model may invent "MOSTLY NEGATIVE"; mixed-signal reviews - the actual hard case - get whichever reading the sampling favors that run.

Good - enumerated labels, boundary-marking examples, pinned shape:

Classify sentiment as POSITIVE, NEGATIVE, or NEUTRAL. Use only these
three labels. A review mixing praise and a product/delivery defect is
NEGATIVE.

Input: "Shipping was fast but the box was crushed."
Output: NEGATIVE

Input: "Exactly what I expected."
Output: NEUTRAL

Input: <review>${userText}</review>
Output:

The examples do the work the adjectives could not: the first one settles the mixed-signal boundary by demonstration.

Step 4: Harden the boundaries

• Delimit user-supplied content with XML-style tags (<review>...</review>) so injected instructions inside the data cannot blur into your instructions.
• For classification, enumerate the exact allowed labels and forbid new ones.
• Prefer positive instructions ("respond in JSON") over negative ones ("don't use prose") - models follow targets better than prohibitions.
• Specify the failure mode explicitly; an unspecified failure mode means the model improvises one per run.

Step 5: Iterate against real inputs and known edge cases

Run the prompt on the collected real inputs, then apply the standard fixes:

• Long context: place the question both before and after the documents.
• Inconsistent JSON: lower temperature, validate against a schema, retry once on parse failure.
• Hallucinated facts: instruct "only use the provided context; cite the sentence."
• Refusals on benign tasks: add a one-line justification of legitimate intent.
• Format drift over long conversations: restate the format in the latest turn; do not rely on the model remembering it.

When a change "seems better," measure it - hand the comparison to llm-evaluation rather than eyeballing three outputs.

Deliverable

Produce a prompt file containing: the one-sentence objective, the four blocks in the Step 2 order, 1-3 boundary-marking few-shot examples, delimited slots for user content, an explicit failure-mode rule, and a note listing the real inputs it was iterated against.

Do NOT

• Do not use quality adjectives ("detailed", "creative", "professional") as instructions - they are unfalsifiable and change nothing; encode the desired property as a rule or an example.
• Do not stuff multiple tasks into one prompt - each added task degrades all of them; split into a chain.
• Do not describe the output format in prose when you can pin it with a literal schema or example.
• Do not leave user-supplied content undelimited - that is the injection surface.
• Do not iterate on imagined inputs; the prompt will overfit to inputs no one sends.
• Do not bury the critical constraint in the middle - put it last, where recency bias makes it stick.

Quality bar

• The objective is one sentence and the prompt serves only it.
• Output shape is pinned by a literal schema or example; a parser or reader gets the same shape every run.
• Every allowed label or enum value is enumerated; new ones are forbidden.
• User content sits inside delimiters; the failure mode is specified.
• Few-shot examples mark decision boundaries, not easy wins.
• Any claimed improvement is backed by a run on real inputs, not a single good sample.