Criteria-by-Criteria Review — The Spot-Check Technique

The Spot-Check Technique is a systematic method for verifying AI-generated proposal content. Instead of reading your draft and hoping you catch errors, you walk through funder criteria one by one and verify that each claim in your proposal is accurate, supported, and aligned.

Why Systematic Verification Matters

When you read a well-written draft, your brain fills in gaps. Smooth prose makes claims feel true even when they aren’t verified. Professional-sounding language creates a false sense of completeness. This is the review trap: the better AI writes, the easier it is to miss errors.

The Spot-Check Technique breaks this trap by forcing you to check specific types of claims against specific sources — not by reading holistically, but by verifying methodically.

The Technique

List the funder's criteria

What is the funder evaluating? Pull the scoring rubric or review criteria. Each criterion becomes a verification target.

Walk each criterion against your draft

For each criterion, find the corresponding content in your proposal. Does the draft address it? Does it address it well? Are the claims supporting it accurate?

Verify specific claims

For each factual claim — a statistic, a date, a program outcome, a population number — trace it to a source. Can you point to the document or dataset that supports this claim?

Flag unsupported claims

If you can't trace a claim to a source, it's either from your institutional knowledge (acceptable, but make a note) or AI-generated without basis (fix it). Don't leave unverified claims in a high-stakes proposal.

Check for completeness

After walking all criteria, is anything missing? Did the AI draft skip a required element? Is any criterion only partially addressed?

What to Spot-Check First

Not all claims carry equal risk. Prioritize verification on:

Statistics and numbers

Every specific number in the proposal: population figures, outcome percentages, budget amounts, timeline dates. These are the most common AI fabrication points.

Program descriptions

Does the proposal accurately describe what your program does, who it serves, and what outcomes it produces? AI sometimes generalizes or embellishes.

Funder-specific claims

Any statement about the funder — their priorities, their past giving, their geographic focus. Errors here signal that you haven't done your research.

Commitments and promises

Any statement that commits your organization to an action, a timeline, a deliverable, or a matching contribution. These become binding if you win.

Watch out

The sneakiest errors aren’t outright fabrications — they’re embellishments. AI takes a real fact and adds plausible-sounding detail. “We served 200 youth” becomes “we served over 200 at-risk youth from predominantly low-income families.” The base fact may be right; the added detail may not be. Check the specifics, not just the general claims.

The Spot-Check Technique isn’t about distrusting AI — it’s about professional responsibility. Every claim in your proposal reflects on your organization. Checking them systematically against funder criteria ensures your proposal is not just well-written but accurate, complete, and aligned with what the funder actually evaluates.

Making It Efficient

The technique sounds time-consuming, but it gets fast with practice. After a few rounds:

You learn which types of claims AI gets right consistently (you verify less)
You learn which types of claims AI embellishes (you verify more)
You develop an eye for the specific patterns that signal unverified content
The whole process takes 30-45 minutes for a standard foundation proposal

Pro tip

Keep your source documents open alongside the proposal during spot-checking. Past proposals, program data, and the RFP should be accessible for quick cross-reference. This turns verification into a 10-second check per claim rather than a 5-minute research project.

Check your understanding

You're spot-checking a needs statement. AI wrote: 'According to county data, 34% of families in the service area live below the federal poverty line, a rate that has increased 8% over the past three years.' How do you verify this?

Key Takeaways

The Spot-Check Technique walks funder criteria one by one, verifying that each claim is accurate, supported, and aligned
Prioritize verification on statistics, program descriptions, funder-specific claims, and commitments
Watch for embellishments — AI often takes a real fact and adds plausible-sounding detail that isn't verified
The technique gets faster with practice as you learn which claim types AI gets right and which it embellishes

Next Lesson

Statistics and specific claims deserve special attention. The next lesson goes deeper into fact-checking — the specific techniques for catching AI fabrications that sound plausible but aren’t real.