Prompt Engineering: Set Clear Goals and Output Constraints

Define precise goals and constraints to get predictable, cost‑effective model outputs; actionable steps and checks to implement now. Try these tactics.

Well-defined goals and output constraints turn unpredictable model responses into reliable outputs. This guide gives practical techniques—templates, structure, tests—to cut token waste while preserving intent and quality.

• TL;DR: define what success looks like, limit outputs, use templates and roles, test with metrics
• Cut unnecessary tokens by prioritizing content and compressing language
• Structure prompts with roles, steps, and examples and measure results to optimize cost and quality

Set clear goals and output constraints

Start by stating the single most important objective for the prompt: what decision will be made or what artifact is required. Include scope, required fields, and hard limits (length, format, tone).

• Objective example: “Produce a 120–150 word product summary for non‑technical customers highlighting benefit, use case, and a CTA.”
• Scope example: “Do not include technical specs; include exactly one CTA sentence.”
• Hard limits: “Maximum 900 characters; plain text only; bulleted list allowed.”

Quick answer

Define the exact output format and constraints up front: tell the model the goal, required elements, and strict length/format limits (e.g., JSON schema or exact word count) to get predictable, parseable responses.

Prioritize content: cut tokens without losing intent

Identify which information is essential versus optional. Rank content by business value and remove or shorten lower‑value elements.

• Essential: user question, required fields, acceptance criteria.
• Nice‑to‑have: extended background, marketing flourishes, multiple variants.
• Cut strategy: eliminate repetition, prefer single examples, collapse lists into compact phrases.

Example: Instead of a long persona paragraph, use a one‑line shorthand: “Persona: busy marketing manager, high-level benefits focus.”

Compress language: templates, macros, and placeholders

Use concise templates to reduce prompt size and ensure consistent outputs. Replace repetitive instructions with placeholders or macros you expand server-side.

{
  "template": "Summary: {benefit}. Use case: {use_case}. CTA: {cta}",
  "limits": "max_chars=700; tone=conversational; format=plain_text"
}

• Use macros for common instructions: {tone=concise}, {format=json}.
• Store long background in a reusable snippet referenced by ID rather than inlining repeatedly.

Structure prompts: roles, steps, and examples

Organize prompts into clear sections: role, goal, step instructions, and one example. This increases consistency and reduces the model’s need to infer context.

• Role: “You are a product copywriter.”
• Goal: single sentence (see above).
• Steps: numbered actions the model should take, e.g., “1) Write headline; 2) Two-sentence summary; 3) CTA.”
• Example: one short input → desired output to show format.

Compact prompt example:

Role: Product copywriter.
Goal: 2-sentence benefit summary for non-technical buyers (max 40 words).
Steps:
1) Headline (6–8 words).
2) Two-sentence summary (max 40 words).
Example:
Input: "Battery case for phones"
Output: "Power on the go — lightweight backup battery. Keeps phones running all day; charges twice fast. Buy now."

Control verbosity: instructions for length and format

Give explicit length constraints and formatting rules; prefer absolute limits (characters, words, tokens) and structured formats (JSON, CSV) for machine consumption.

• Length directives: “Exactly 40–50 words”, “Max 3 bullets”, “<= 280 characters".
• Format directives: “Return only JSON matching this schema”, “No additional commentary”.
• Enforce with negative instruction: “Do not add explanations, or metadata.”

Example JSON schema instruction:

Return JSON:
{
  "headline": "string (6-10 words)",
  "summary": "string (max 40 words)",
  "cta": "string"
}
Do not add extra keys or text.

Measure quality and cost: testing and metrics

Track output quality and token cost with simple experiments. A/B test prompt variants and measure both accuracy and tokens consumed.

Run short experiments (100–500 samples) comparing: template vs inline, with vs without examples, and with different length caps. Use acceptance rate and tokens per accepted output to compute cost per usable output.

Common pitfalls and how to avoid them

• Overloading prompts with background — remedy: move to stored snippets and reference by ID.
• Ambiguous goals — remedy: state a single measurable objective and acceptance criteria.
• No format enforcement — remedy: require strict schema and include a short example output.
• Too many examples — remedy: include 1 concise example that reflects edge cases only.
• Relying on length words only — remedy: prefer character or token limits for greater control.

Implementation checklist

• Define one clear objective and acceptance criteria for each prompt.
• Create concise templates with placeholders and store long context externally.
• Structure prompts: role, goal, ordered steps, one example.
• Set hard limits for length and format; prefer JSON/schema for parsable outputs.
• Run A/B tests, track tokens, acceptance rate, and edit time.
• Iterate: shorten prompts, keep examples minimal, and enforce schema.

FAQ

Q: How strict should length constraints be?

A: Use strict limits for machine‑consumable outputs; allow a small tolerance for human text generation when naturalness matters.

Q: One example or multiple?

A: Start with one high‑quality example that matches the desired edge case; add a second only if necessary.

Q: When to use JSON vs plain text?

A: Use JSON for downstream parsing and automation; plain text is fine for human‑readable copy where slight variation is acceptable.

Q: How often should I re‑test prompts?

A: Re‑test after any model change, major prompt edit, or quarterly for steady state to catch drift.

Q: How do I measure cost per usable output?

A: Multiply average tokens per accepted response by token unit cost, then divide by acceptance rate (or compute cost only on accepted samples).