Why Your AI Resume Sounds Generic: The Missing Inputs That Create Specificity

When an AI-generated resume feels generic, people assume the model isn't smart enough.

Most of the time, the model is behaving rationally: it refuses to hallucinate specifics you didn't provide.

The fastest fix is not a better prompt, it's adding two real numbers and one concrete project detail to your base CV.

This post gives you a "Specificity Packet" you can add to your base resume so any tailoring tool has real material to work with.

Why generic happens (the boring truth)

Generic language is what you get when inputs lack:

• scope (how big, how many, how often)
• outcomes (what changed)
• constraints (time, cost, reliability)
• decisions (what you chose and why)

If your base CV says:

• "Worked on pipelines."

Any responsible generator will output:

• "Worked on pipelines to support business needs."

Because it can't invent more.

The mechanism: Specificity Inputs (5 types)

You want to inject at least 3 of these into each major role:

1) Numbers

• volumes, latency, dollars, percentages, time saved

2) Ownership nouns

• "owned", "led", "shipped", "migrated", "implemented"

3) System nouns

• the real artifacts: "SLA", "alerting", "backfill", "model", "schema", "permissions"

4) Constraint nouns

• "uptime", "compliance", "cost", "deadline", "risk"

5) Decision nouns

• "chose X over Y", "standardized", "reduced complexity"

The artifact: The Specificity Packet (copy/paste)

Add this to your base CV as a private scratch section while you rebuild bullets. Then convert into final bullets.

```

SPECIFICITY PACKET (per role)

• What I owned end-to-end (1 sentence):

_______________________________

• Scale or volume (pick 1–2):

Records/day: _______

Users/teams served: _______

Runtime/latency: _______

Cost impact: _______

• Reliability / quality:

Incidents reduced: _______

SLA / uptime: _______

Tests/validation added: _______

• A decision I made (and why):

"Chose _______ over _______ because _______"

• One concrete outcome:

"Result: _______"

```

If you are using HyperApply and the output is too generic, the product guidance is consistent with this exact idea: https://hyperapply.app/faq/how-to-make-results-more-specific

Two quick upgrades that almost always work

Upgrade 1: Replace "worked with" with ownership + outcome

From:

• "Worked with stakeholders to build reports."

To:

• "Owned weekly reporting pipeline, aligned metrics with stakeholders, reduced manual reporting time by 4 hours/week."

Upgrade 2: Add one constraint

From:

• "Built ETL pipelines."

To:

• "Built ETL pipelines with SLA monitoring and backfills, reduced late data incidents by 60%."

For additional fixes (including "too salesy" outputs), use: https://hyperapply.app/docs/common-output-quality-fixes

7–14 day execution plan

Days 1–2:

• Add a Specificity Packet for your last 2 roles.
• Rewrite 6–10 bullets with real numbers and ownership.

Days 3–7:

• Apply to 5 roles.
• After each application, add one missing specificity point back into the base CV so the next output improves automatically.

Base CV workflow: https://hyperapply.app/docs/add-your-base-cv

Days 8–14:

• Build a "proof bank" for your target role type.
• Stop relying on the generator to infer your seniority. Give it the raw material.

If partial job descriptions are causing drift, fix the input first: https://hyperapply.app/docs/how-to-handle-missing-or-partial-job-descriptions

Where HyperApply fits

HyperApply is a user-controlled tailoring assistant: it generates a tailored draft from the job listing you're already viewing, then you review and submit manually. It does not auto-apply.

Generate workflow: https://hyperapply.app/docs/how-to-generate-a-tailored-cv-from-a-job-post

Best practices: https://hyperapply.app/docs/recommended-workflow-for-best-results

Keyword honesty rule: https://hyperapply.app/docs/how-to-avoid-keyword-stuffing

Takeaway

AI isn't failing you. Your inputs are thin.

Give your resume a Specificity Packet, and tailored drafts will stop sounding like generic templates.