AI Prompt Optimizer
Your prompt, actually fixed: with a diagnosis of exactly what was wrong, not just a different rewrite. Just enter your current prompt, what went wrong, goal.
How It Works
Paste your current prompt and describe what's going wrong with the output (too generic, inconsistent format, wrong tone, too long/short). The optimizer diagnoses the specific gaps and returns an improved version plus a short explanation of each change.
What to Provide
| Input | What to enter |
|---|---|
| Current prompt | Exactly what you've been using |
| Problem | What's wrong with the output you're getting |
| Example output (optional) | A real output that shows the problem |
Optimized Prompt + Diagnosis
Replace [[tokens]] with your details. This is the finished deliverable.
Diagnosis
| Issue found | Why it's causing the problem |
|---|---|
| [[e.g. No role specified]] | [[The model defaults to generic tone with no expertise framing]] |
| [[e.g. No output format]] | [[Output structure varies unpredictably run to run]] |
| [[e.g. No length constraint]] | [[Responses run long and pad with filler]] |
Before
[[Your original prompt, pasted as-is]]After
[[Rewritten prompt with role, specific constraints, explicit output format, and any needed examples added]]What Changed and Why
- [[Added role: "You are a [[specific expert]]": narrows tone and expertise level]]
- [[Added constraint: [[specific limit]]: prevents [[the specific problem you had]]]]
- [[Added output format: [[exact structure]]: makes output consistent run to run]]
Worked Examples
Example 1: Too Generic Output
Before: "Write a LinkedIn post about our new feature."
Diagnosis: No audience, no hook structure, no length guidance.
After: "You are a B2B SaaS content writer. Write a LinkedIn post announcing [[feature]] for [[audience]]. Open with a specific pain point, not a generic statement. 100-150 words. End with a question to drive comments."
Example 2: Inconsistent Format
Before: "Analyze this data and tell me what's interesting."
Diagnosis: No output structure specified, so responses vary from a paragraph to a bulleted list unpredictably.
After: "Analyze this data. Output exactly: 1) Three notable trends as bullets, 2) One risk to flag, 3) One recommended next action."
Common Mistakes to Avoid
- Rewriting without diagnosing: fixing the wrong thing because the real issue wasn't identified
- Adding constraints that don't address the actual reported problem
- Over-constraining: so many rules the model can't satisfy all of them
- Not testing the optimized version on 2-3 real inputs before trusting it
- Losing the original intent while "fixing" the prompt
A good optimization tells you exactly what was broken and why the fix works: not just a different-sounding prompt.
Illustrative preview: your actual result is built from your inputs.
How it works.
Paste your prompt and what's going wrong: get a diagnosis and a fixed version, not just a reworded guess. Free, no signup.
Draft my optimized prompt
A diagnosed fix with before/after comparison: not just a different-sounding prompt.
What good looks like.
What it must include
- 01A specific diagnosis of what was actually wrong, not just a rewrite
- 02The exact fix mapped to the exact problem you reported
- 03A before/after comparison so you can see what changed
- 04Testing guidance before you trust the new version
Signals of expertise
- ★Diagnoses the specific issue before rewriting
- ★Shows before/after so the fix is visible
- ★Explains why each change addresses the reported problem
Common mistakes
- ×Rewriting without diagnosing the actual issue
- ×Over-constraining until the model can't satisfy everything
- ×Not testing the optimized version before trusting it
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