ChatGPT Prompt Generator
Updated for the GPT-5.6 family — Luna, Terra and Sol. Generate optimized prompts using OpenAI's PTCF framework (Persona, Task, Context, Format) plus the practice that matters most on this generation: subtractive design, where every instruction is stated exactly once. Fill in your role, deliverable, audience and constraints, and let AI craft a lean brief with explicit acceptance criteria.
Be specific about what you want the AI to do
Generated Prompt
Fill in the form and click "Generate" to create an optimized ChatGPT (GPT-5.6) prompt.
Tip: The more specific your task description and context, the better the generated prompt will perform.
ChatGPT (GPT-5.6) Tips
- • Say each instruction exactly ONCE — OpenAI found that removing repeated instructions raised scores 10-15% while cutting tokens 41-66%
- • Use the PTCF framework: Persona, Task, Context, Format — still the proven baseline for ChatGPT
- • Smaller scope wins: shrink any prompt that feels overloaded into one clear deliverable
- • Tell ChatGPT the audience, the format, AND the decision criteria — most prompts fail because one of these is missing
- • Skip "think step by step" unless you want the reasoning shown — GPT-5.6 reasons internally and depth is set by the effort selector
- • Sharper acceptance criteria beat a higher reasoning setting: say how a correct answer is checked
- • Use few-shot examples for pattern-matching tasks (translations, classifications, formatting), not for open-ended reasoning
ChatGPT (GPT-5.6) Prompt Templates
Copy-ready starting points for the most common jobs people bring to this model. Swap the [BRACKETED] parts for your own details, or use them as a shape to imitate when you write your own.
Competitive Teardown
BusinessA decision-ready comparison brief, scoped tightly enough that the model does not flail.
You are a [INDUSTRY] strategy analyst. Write a competitive teardown of [COMPETITOR A], [COMPETITOR B] and [COMPETITOR C] for [AUDIENCE, e.g. our exec team] who must decide [DECISION]. Context: we sell [PRODUCT] to [CUSTOMER SEGMENT]. Our current edge is [EDGE]; our known weakness is [WEAKNESS]. Acceptance criteria: - One paragraph per rival covering positioning, pricing posture and the single reason we win or lose against them. - A closing table ranking all three by threat level with a one-line justification each. - Every claim names the evidence behind it or is marked as an assumption. Format: Markdown, under [WORD COUNT] words, no jargon.
Code Review Brief
EngineeringReviews that return prioritised, actionable findings instead of generic advice.
You are a senior [LANGUAGE] engineer reviewing code for a [CONTEXT, e.g. production payments service]. Review the code below for correctness bugs, security issues and maintainability problems, in that order of priority. Stack: [LANGUAGE + VERSION], [FRAMEWORK], [RUNTIME]. Conventions: [LINTER/STYLE GUIDE]. Acceptance criteria: - Every finding names the file and line, the concrete failure it causes, and a fix. - Findings are ordered most severe first. - Say explicitly if you find nothing in a category rather than inventing filler. Format: numbered list, one finding each, code snippets in fenced blocks. Code: [PASTE CODE]
Long-Form Article
WritingStructured writing with a defined reader and a real angle.
You are a [NICHE] writer with hands-on experience in [DOMAIN]. Write a [WORD COUNT]-word article titled "[WORKING TITLE]" for [AUDIENCE], who already know [ASSUMED KNOWLEDGE] but not [THE GAP]. Angle: [THE SPECIFIC ARGUMENT OR INSIGHT]. Do not write a neutral overview. Acceptance criteria: - Opens with a concrete scenario, not a definition. - Each section makes one claim and supports it with a specific example. - Ends with something the reader can do this week. Format: Markdown with H2 sections, [NUMBER] sections, [TONE] tone.
Data Analysis Request
AnalysisAnalysis where the decision criteria are stated up front so trade-offs are made correctly.
You are a data analyst supporting [TEAM]. Analyse the dataset below and answer: [THE SPECIFIC QUESTION]. Context: this feeds [DECISION]. We care most about [PRIMARY METRIC]; [SECONDARY METRIC] is a tiebreak. Known data caveats: [CAVEATS]. Acceptance criteria: - Lead with the answer in one sentence, then the supporting analysis. - Quantify every claim and state the confidence. - Flag anything the data cannot support rather than estimating around it. Format: executive summary, then a findings table, then caveats. Data: [PASTE DATA]
Support Reply Rewriter
OperationsPattern-matching work — the one place few-shot examples still earn their keep.
You are a customer support lead for [PRODUCT]. Rewrite the draft reply below to match our voice: [VOICE, e.g. warm, direct, never defensive]. Rules: acknowledge the issue first, give the concrete next step with a timeframe, never blame the customer, never promise a date engineering has not committed to. Example of a good reply: "[PASTE ONE REAL EXAMPLE THAT MATCHES YOUR VOICE]" Format: plain email text under [WORD COUNT] words, no subject line. Draft: [PASTE DRAFT]
Meeting Notes to Actions
OperationsTurns a transcript into something assignable, with explicit handling for ambiguity.
You are a chief of staff. Turn the transcript below into a decisions-and-actions record for [AUDIENCE]. Acceptance criteria: - Separate DECISIONS (already made) from ACTIONS (still to do) from OPEN QUESTIONS. - Every action names an owner and a due date drawn from the transcript. - Where an owner or date was never stated, write "unassigned" rather than guessing. Format: three Markdown sections, bullet points, no preamble. Transcript: [PASTE TRANSCRIPT]
ChatGPT Model & Prompting Update Log
GPT-5.6 — Luna, Terra & Sol
● LatestJuly 9, 2026- OpenAI replaced the single flagship with a three-tier family: Luna (fastest and cheapest, now the free-tier default), Terra (balanced everyday work, matching GPT-5.5 quality at about half the cost) and Sol (the flagship for the hardest coding, long agentic runs, science and security research).
- Sol is OpenAI's strongest coding and cybersecurity model to date, scoring 80 on the Artificial Analysis Coding Agent Index v1.1 at maximum reasoning — while using less than half the output tokens and costing roughly a third less than the closest competitor.
- The whole family is markedly more token-efficient: Sol is about 54% more efficient than the previous generation on coding tasks.
- A new Ultra setting coordinates several agents across parallel workstreams to finish complex tasks faster.
- Prompting changed with it. In OpenAI's own testing, stripping repeated and overlapping instructions out of a prompt RAISED evaluation scores by 10-15% while cutting tokens 41-66% and cost 33-67%. Repetition now hurts.
- A better-specified prompt beats a higher reasoning setting — the guidance is to improve the evidence and the acceptance criteria rather than demanding more exhaustive thinking.
- ChatGPT Work shipped alongside the model family.
Ultrafast mode preview
August 13, 2026- OpenAI previewed Ultrafast mode for GPT-5.6 Sol, running the flagship at up to 14x the standard speed.
- Free users moved to GPT-5.6 Luna as the default model, with unlimited text chats.
How to Use the Prompt Generator
Define Your Task
Select a task category, describe what you want ChatGPT to do, and assign a role or persona. The more specific you are, the better the generated prompt will perform.
Set Context & Constraints
Add background context, choose your desired tone and output format, specify your target audience, and set any constraints like word limits or things to avoid.
Generate & Use
Click "Generate" to get an optimized prompt following best practices. Copy it and paste directly into ChatGPT for dramatically better results.
Frequently Asked Questions
What is a ChatGPT prompt generator?
A ChatGPT prompt generator helps you create optimized, well-structured prompts that produce better results from ChatGPT. Instead of writing vague requests, it structures your inputs into prompts that follow proven prompt engineering best practices — including role assignment, context setting, output formatting, and constraint specification.
How does this tool improve my ChatGPT results?
It applies OpenAI's recommended PTCF framework — Persona, Task, Context, Format — plus the practices that matter on GPT-5.6: subtractive design (every instruction stated exactly once), explicit audience, explicit decision criteria, concrete acceptance criteria, and tight scope. Many prompts fail because the audience or the format was never named, or because the same rule was repeated three times; this tool fixes both.
What is the PTCF framework?
PTCF stands for Persona (who ChatGPT should act as), Task (the one specific deliverable), Context (the why, the for-whom, the constraints), and Format (the exact output shape — table, JSON, bulleted list, etc.). It is OpenAI's officially recommended baseline for getting consistent quality from ChatGPT.
Which GPT-5.6 model should I use — Luna, Terra or Sol?
Luna is the fastest and cheapest, and it is the default for free ChatGPT users — good for high-volume, straightforward work. Terra is the balanced everyday choice, delivering roughly GPT-5.5 quality at about half the cost. Sol is the flagship: reach for it on the hardest coding, long multi-step agent runs, scientific research and security work. A useful habit is to keep your current tier as the baseline and try one step down — GPT-5.6 is efficient enough that the cheaper tier often holds quality.
What changed about prompting with GPT-5.6?
The biggest shift is subtractive design. In OpenAI's own testing, stripping repeated and overlapping instructions out of a prompt raised evaluation scores by 10-15% while cutting total tokens 41-66% and cost 33-67%. Saying something three times for emphasis now makes the output worse, not better. The second shift is that a better-specified prompt beats a higher reasoning setting — instead of demanding exhaustive thinking, sharpen the evidence and the acceptance criteria. Our generator applies both automatically.
Is this tool free to use?
Yes. You get 1 free prompt generation per day with no signup required. For unlimited access, sign up for a Promptslove membership which includes all AI tools and 20,000+ premium prompts.
Can I use these prompts with other AI tools?
Yes — the generated prompts follow universal prompt engineering principles and work well with Claude, Gemini, Grok, Kimi, Mistral, MiniMax, Qwen, DeepSeek, and Perplexity. PTCF, role assignment, context, and explicit format are model-agnostic.
What makes a good ChatGPT prompt?
A good ChatGPT prompt names: (1) a clear persona, (2) one specific task, (3) the relevant context including audience and decision criteria, (4) the exact output format and length, (5) explicit constraints — and states each of them exactly once. On GPT-5.6 that last part matters as much as the rest: repetition and overlapping restatement measurably lower output quality, so a lean brief beats a padded one.
Should I use chain-of-thought prompting?
Much less than it used to. GPT-5.6 reasons internally and its depth is controlled by the reasoning-effort selector, not by prompt wording, so adding "think step by step" purely to make it think harder just spends tokens. Keep it only when you genuinely want the reasoning shown in the output — for a worked derivation, an audit trail, or a plan you intend to review. Modern ChatGPT consolidates context well, so keep related instructions in one prompt rather than fragmenting them.
How many prompts can I generate for free?
You get 1 free prompt generation every 24 hours. This resets automatically. For unlimited generations, consider signing up for a Promptslove membership.
Want Unlimited AI Prompt Generation?
Get unlimited access to all AI tools, 20,000+ premium prompts, courses, and resources designed to maximize your creative output.
