Build production-ready AI prompts

Create concise, unambiguous, self-contained prompts for LLMs. Add language, task, context, and constraints to improve precision and reliability.

How to use

1. Select the output language in {lang}.
2. Define the goal and deliverable in {task}.
3. Provide key background, audience, and examples in {context}.
4. Set tone, format, length, style, and do/don't rules in {constraints}.
5. Generate, review the checklist, and copy the final prompt.

Best practices

• State the role, steps/strategy, input expectations, and output format.
• Specify success criteria and boundaries; avoid ambiguity.
• Use measurable requirements (length, style, structure).

Output checklist

• Role/instructions
• Steps/strategy
• Input expectations
• Output format
• Evaluation criteria and boundaries

Steps For Writing Effective AI Prompts

Clear, outcome-focused prompts turn general AI models into practical assistants. Use the steps below to reduce ambiguity, control tone and format, and consistently get reliable results.

• Define the outcome and audience
• Assign a role and perspective
• State the core task clearly
• Provide context and data
• Specify format and length
• Set constraints and style
• Use examples and counterexamples
• Ask for verification steps
• Define acceptance criteria
• Iterate and test
• Reusable prompt template
• Common mistakes
• Quick checklist
• Mini examples
• FAQ

1) Define the outcome and audience

Start with the goal and who will read or use the output. Name the success metric: clicks, readability, accuracy, conversion, or adherence to a specification. Outcome-first framing keeps the model focused and reduces revisions.

2) Assign a role and perspective

Assigning a role anchors tone and decisions. Examples: senior technical writer, growth marketer, compliance reviewer, helpful tutor. If helpful, add domain constraints such as region, industry, or regulation.

3) State the core task clearly

Use a single sentence to say exactly what you want. Examples: write a 700-word explainer for non-technical readers; summarize the report into 5 bullet points; produce an HTML body with h2 headings; draft 3 alternative titles under 60 characters.

4) Provide context and data

Give the model the knowledge you want it to use: product facts, source excerpts, brand voice, user pain points, competitor angles. If you reference links, paste the relevant content since models may not fetch URLs.

5) Specify format and length

Define the output container and structure. Examples: HTML body only; JSON with keys title, meta, body; bullet list; table; 400–600 words; one-paragraph abstract; headline plus subhead.

6) Set constraints and style

Control tone, reading level, keywords, and do nots. Examples: friendly but authoritative; avoid hype; use active voice; include target keywords in h2; Flesch grade 8–9; no first-person singular; cite assumptions and limitations.

7) Use examples and counterexamples

Few-shot examples guide structure and quality. Provide one or two short examples of good output and optionally one counterexample to avoid. Ensure examples match your target audience and format.

8) Ask for verification steps

Request a brief self-check or quality checklist rather than a full chain-of-thought. Example: before final output, verify length, keywords, and formatting; list any missing inputs in one line.

9) Define acceptance criteria

Spell out pass-fail rules: exact fields present, headings include keyword, zero placeholder text, all claims traceable to provided sources, complies with policy, names spelled correctly.

10) Iterate and test

Prompt engineering is iterative. A/B test phrasing, add or remove constraints, and measure results. Keep a prompt library with version notes, edge cases, and known-good examples.

11) Reusable prompt template

Role: [role and domain]
Goal: [what success looks like]
Audience: [who will use or read this]
Task: [single-sentence instruction]
Inputs: [facts, excerpts, data]
Format: [HTML body only / JSON fields / list / table]
Style: [tone, reading level, brand voice]
Constraints: [length, do and do not]
Examples: [1–2 short examples]
Verification: [brief checklist]
Acceptance criteria: [pass-fail rules]
Deliverable: [exact output to return]

12) Common mistakes

• Vague goals such as write about X without audience or success criteria
• Missing format specs, which leads to cleanup work
• Overloading the model with irrelevant context
• Asking for internal chain-of-thought instead of short verifications
• Not stating constraints like length, tone, and banned phrases
• Changing multiple variables at once during iteration, making results hard to compare

13) Quick checklist

• Outcome and audience defined
• Role assigned
• Single clear task
• Relevant context provided
• Format and length specified
• Style and constraints set
• Examples included
• Verification and acceptance criteria included
• Iterated and tested

14) Mini examples

SEO article section

Role: senior SEO writer
Goal: increase organic traffic for the term effective AI prompts
Audience: marketers and content leads
Task: write an HTML body section with h2 and h3
Inputs: target keywords, brand voice: practical and concise
Format: HTML body only
Constraints: 400–500 words; include the keyword in one h2
Verification: confirm length and keyword placement
Acceptance criteria: no placeholders; clear subheadings

Data summarization

Role: analyst
Task: summarize the dataset notes into 5 bullets
Inputs: pasted notes
Format: bullet list
Constraints: max 120 words; plain language

Product messaging

Role: product marketer
Task: draft 3 headlines under 60 characters and 3 body lines under 120 characters
Inputs: product benefits and differentiators
Format: numbered list
Constraints: avoid jargon and superlatives

15) FAQ

How long should prompts be? As long as needed to express goal, context, constraints, and format. Brevity is good, clarity is better.

Do I need examples? Not always, but examples greatly improve structure and tone when quality matters.

Should I ask for step-by-step reasoning? Prefer brief checklists or short rationales to avoid verbose or sensitive internal reasoning.

What if I lack context? Ask the model to list missing inputs first, then proceed once you supply them.

How do I keep results consistent? Reuse a tested template, lock format and constraints, and maintain a prompt library with versions and outcomes.

Effective prompts are specific, structured, and measurable. Start with the outcome, supply only relevant context, lock the format, and iterate until your results are predictable.