5 Prompt Engineering Tricks That Made My AI Resume Checker 10x Better

I built an AI resume checker. The first version produced generic feedback nobody would pay for. These 5 prompt engineering tricks fixed it.

Trick 1: Negative constraints

The single biggest improvement came from telling the AI what NOT to do:

Do NOT:
- Use generic phrases like "strong communication skills"
- Suggest adding "action verbs" without examples
- Comment on formatting unless it's genuinely broken
- Give advice that would apply to any resume

This eliminated 80% of generic output.

Trick 2: Role specificity

Bad: "Act as a recruiter"
Good: "Act as a technical recruiter at a Series B startup who has reviewed 500+ engineering resumes"

The more specific the role, the more targeted the feedback.

Trick 3: Output format constraints

Output format:
- Score: X/10 (be honest, not generous)
- Top 3 strengths (with specific line references)
- Top 3 weaknesses (with specific fixes)
- Missing elements (if any)
- Overall recommendation: interview / maybe / pass

Structured output is scannable and actionable.

Trick 4: Context injection

Context:
- Target role: [JOB TITLE]
- Target company type: [STARTUP/ENTERPRISE/AGENCY]
- Experience level: [JUNIOR/MID/SENIOR]
- Industry: [SPECIFIC INDUSTRY]

Every piece of context narrows the output from generic to specific.

Trick 5: The "honest critic" constraint

Be a harsh but fair critic. Do not sugarcoat.
If the resume is bad, say so and explain why.
If a section is missing, flag it.
Your goal is to help them get hired, not to make them feel good.

This single constraint transformed feedback quality. People pay for honest feedback, not validation.

These same principles apply to any AI prompt — not just resume checking. Specificity, constraints, and honesty produce output worth paying for.

The full collection of 200+ tested prompts: https://innovate01.gumroad.com/l/qevvy ($9 starter pack)