The Spot-Check Technique for AI-Generated Content

Self-review catches big-picture issues. The Spot-Check Technique is a more focused method — a systematic way to verify that AI-generated content is accurate, grounded in your actual data, and aligned with what the funder asked for.

Why Spot-Checking Matters

AI-generated text has a specific failure mode: it can sound completely confident while stating something that’s slightly (or entirely) wrong. Unlike a human draft where uncertainty often shows up as hedging language or obvious gaps, AI fills in the blanks with plausible-sounding content.

The most dangerous AI errors aren’t wild fabrications — those are easy to catch. The dangerous ones are subtle: a statistic from the wrong year, a program description that’s almost right but includes a detail you didn’t provide, a claim about a partnership that doesn’t quite exist.

The Spot-Check Technique is designed to catch exactly these issues.

The Method

Work through your proposal criterion by criterion, matching the funder’s requirements against what the AI wrote.

List the verification points

Check each point against your source material

Flag the three categories

Fix and re-verify

Where to Focus Your Spot-Checks

You don’t need to spot-check every sentence. Focus on:

Data-heavy sections

Organizational claims

Budget-narrative connections

Funder-specific language

Building the Habit

Spot-checking adds time to the review process, but it’s time well spent. A single fabricated statistic discovered by a reviewer can undermine the credibility of your entire proposal. A claim about a partnership that doesn’t exist can damage a funder relationship.

Pro tip

As you build this practice, you’ll notice patterns in where AI tends to embellish. Some people find AI is consistently accurate with organizational descriptions but less reliable with statistics. Others find the opposite. Tracking your own patterns makes future spot-checks faster.

Using AI to help you spot-check

There’s an important distinction here between general-purpose chatbots and purpose-built grant tools.

A general chatbot can check internal consistency — “does the participant count in section 3 match section 7?” — but it has no way to verify claims against your actual data. It only sees what you paste in.

A purpose-built tool that already has your source documents can do something more valuable: verify AI-generated claims against your actual organizational data. When it flags that a program description doesn’t match your uploaded program report, or that a statistic doesn’t appear in any of your workspace documents, that’s genuine verification — not just consistency checking.

Either way, you can use AI to:

Compare your proposal against the RFP criteria and flag gaps
Check that budget numbers match across narrative and budget sections
Identify claims that aren’t supported by your source documents

In Grantable

In Grantable: Because your workspace contains your organizational documents, program data, and the RFP, Grantable can verify proposal claims against your actual source material — not just check whether the proposal is internally consistent. It flags where the draft says something your documents don’t support.

Human eyes on the critical claims — statistics, dates, organizational facts — are still non-negotiable. But purpose-built verification catches the subtle errors faster.

Track D covers AI risks and verification in much more depth. For now, the Spot-Check Technique gives you a practical tool you can use immediately.

Check your understanding

During a spot-check, you find that the AI wrote 'Our after-school program served 450 youth last year' but your actual program report says 387. This is best categorized as:

Key Takeaways

AI's failure mode is confident-sounding inaccuracy — subtle errors that are easy to miss
The Spot-Check Technique: list verification points, check each against source material, categorize as verified/adjusted/fabricated
Focus spot-checks on data-heavy sections, organizational claims, budget connections, and funder-specific language
AI can assist with verification, but human review of critical claims is non-negotiable

Next Lesson

The proposal is written and reviewed. Now for the less glamorous but equally important part: actually getting it submitted.