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Hi,
I just found openAI prompt optimizer
https://platform.openai.com/chat/edit?models=gpt-5&optimize=true
Has someone use it for other than technical and coding prompts?
Not sure if it can work as a general prompt optimizer or just for coding.
It refactors your prompt to remove contradictions, tighten format rules, and align with GPT-5’s behavior. The official GPT-5 prompting guide explicitly recommends testing prompts in the optimizer, and the cookbook shows how to iterate and even save the result as a reusable Prompt Object.
Link (Optimizer):https://platform.openai.com/chat/edit?models=gpt-5&optimize=true OpenAI Platform
More from OpenAI on why/when to use it: GPT-5 prompting guide + optimization cookbook. OpenAI Cookbook
Why this matters
GPT-5 is highly steerable, but contradictory or vague instructions waste reasoning tokens and degrade results. The optimizer flags and fixes these failure modes.
You can version and re-use prompts by saving them as Prompt Objects for your apps.
10-minute workflow that works
Paste your current prompt into the optimizer and click Optimize. It will propose edits and explain why.
Resolve contradictions (e.g., tool rules vs. “be fast” vs. “be exhaustive”), and add explicit output formatting.
Set reasoning effort to match the task (minimal/medium/high) to balance speed vs. depth.
Add a brief plan → execute → review loop inside the prompt for longer tasks.
Save as a Prompt Object and reuse across chats/API; track versions as you iterate.
Copy-paste mini-template (drop into the optimizer)
pgsqlCopyEditPurpose — Goal + "Done" + allowed tools. Reasoning_effort: <minimal|medium|high>. Role — Persona + strict tool rules; ask questions only if critical. Order of Action — Plan → Execute → Review; end with a short “Done” checklist. Format — Markdown sections, bullets, tables/code; target length; restate every 3–5 turns. Personality — Tone (confident/precise), verbosity (short/medium/long), jargon level. Controls — Max lookups <n>; if tools fail, retry once then proceed with labeled assumptions.
(The GPT-5 guide notes verbosity and reasoning controls; use them deliberately.) OpenAI Cookbook
Best practices with GPT-5 + the optimizer
Kill contradictions first. The optimizer is great at spotting conflicting instructions—fix them before anything else.
Right-size “reasoning_effort.” Use minimal for latency-sensitive work, high for complex multi-step tasks.
Constrain the format. Specify headings, bullet lists, and tables; remind the model every 3–5 turns to maintain structure.
Plan before doing. Prompted planning matters more when reasoning tokens are limited.
Use the Responses API for agentic flows to persist reasoning across tool calls.
Version your prompts. Save the optimized result as a Prompt Object so your team can reuse and compare.
Add lightweight evals. Pair the optimizer with Evals/“LLM-as-judge” to measure real improvements and regressions.
Tune verbosity. Use the new verbosity control (or natural-language overrides) to match audience and channel.
What to watch out for
Don’t over-optimize into rigidity—leave room for the model to choose smart tactics.
Quick start
Open the optimizer → paste your prompt → Optimize.
Apply edits → add plan/format/controls → Save as Prompt Object.
Test with a few real tasks → track results (evals or simple checklists) → iterate.
If you need some prompt inspiration you can check out all my best prompts for free at Prompt Magic
OpenAI released a Prompt Optimizer for ChatGPT-5. You paste your prompt, choose a goal (accuracy, speed, brevity, creativity, safety), and it rewrites the prompt into a clean template with role, task, rules, and output format. It also lets you A/B test the original vs the optimized version and save the result as a reusable Prompt Object.
Links
Optimizer: https://platform.openai.com/chat/edit?models=gpt-5&optimize=true
How to use
Paste your prompt → click Optimize.
Remove conflicts, set reasoning level (low/medium/high), define output format.
Save as a Prompt Object and reuse it. Run the A/B test and keep the winner.
Quick templates
Study: Explain [topic]. Output: overview, 3 key points, example, 3‑line summary. Include sources.
Code: Fix this [language] snippet. Output code only with 3 comments explaining changes.
Research: Summarize links into 5 insights, 2 limits, 1 open question, plus 3 refs.
Data: Convert text to strict JSON array with fields X/Y/Z; drop incomplete rows.
Tips
Fix contradictions first.
Be explicit about structure and length.
Match reasoning level to task complexity.
Version prompts and track improvements with the A/B tool.
OpenAI just shipped a free Prompt Optimizer for ChatGPT 5 and it’s the rare tool that actually saves time. Paste your chaos prompt. Pick what you care about (accuracy, speed, brevity, creativity, safety). Boom—clean, structured prompt with role, constraints, and exact output format. It even lets you A/B your original vs the optimized version so you can keep receipts.
Grab it
Optimizer: https://platform.openai.com/chat/edit?models=gpt-5&optimize=true
Why this slaps
Kills contradictions (“be brief” + “explain every step”) that tank results.
Adds clear sections: Role → Task → Constraints → Output → Checks.
Reasoning slider so you don’t burn tokens on easy tasks.
Save as a Prompt Object and reuse anywhere—share with friends or your team.
60‑second recipe
Paste your prompt → Optimize.
Pick Accuracy (or Brevity if you hate fluff).
Specify format: headings, code blocks, tables, or strict JSON.
Run A/B on two real tasks → keep the winner → save as preset.
Plug‑and‑play starters
Tutor: “Teach [topic]. Output: overview, 3 key ideas, example, 3‑line TL;DR. Cite sources.”
Debug: “Fix this [language] code. Return code only with 3 inline comments.”
Research: “Summarize links into 5 insights, 2 caveats, 1 open question + 3 references.”
Data: “Convert text to strict JSON array (fields X/Y/Z). Drop incomplete rows. No prose.”
Pro tips
Be explicit. Structure beats vibes.
Match reasoning to difficulty (low = fast, high = deep).
Version your prompts and track wins with the A/B tool.