Why AI generates almost-right requirements

AI generates almost-right requirements because it relies on patterns from how requirements usually look, not your product's actual terminology, decisions, and rules. The result is a spec that sounds correct but encodes the wrong meaning.

Almost right is worse than wrong here. A wrong requirement gets flagged; an almost-right one gets approved, built, and discovered weeks later when it doesn't behave the way anyone assumed. And because the output is well-formed and fluent, it often passes review even when the underlying assumptions are wrong.

A concrete case: an AI writes "users can archive projects" when your product actually has a concept called "freeze workstreams" – a different term, with different rules about who can do it and what stays visible afterward. The sentence reads fine. It's wrong in three ways at once – the wrong word, the wrong rule, and a meaning your team would never have written.

Picture a product manager – call her Sally – who joins a new team and gets handed Jira and a wiki. She spends weeks figuring out what "Everything" means in the product, because here it isn't a common word, it's a specific screen. An AI tool writing requirements has Sally's first-week problem on every story, except it never asks. It guesses, confidently, and moves on.

Two things drive the drift. The tool doesn't know the product's terminology, so it uses words in their generic sense. And it doesn't know the decisions behind the work, so it writes requirements that contradict choices the team already made. This is the AI context problem in practice – the tool is generating from a generic understanding of requirements, not your product's actual context.

Only 8% of engineers say a ticket gives them everything they need to start, and ambiguous or missing acceptance criteria is the top cause of delay and rework they report (Context Gap Report). The rest of the friction shows up downstream – as clarifying questions, rework, and implementations that don't match what was meant.

A note on cause: that missing product context is the primary driver of this drift is a strong argument, but not the only one – review gaps and thin acceptance criteria contribute too. The fix below helps regardless of how you weight them.

When AI has access to your product's terminology and the decisions behind past work, it stops guessing and starts writing from the same shared understanding your team uses. That terminology lives in a Glossary; those decisions live in AI Context; both reach the tool through an MCP server.

This is what missing AI agent context looks like in practice – supply the reasoning and the output comes back as specs and stories that match your product, with fewer defects and fewer mid-cycle discoveries. Better AI requirements gathering starts with giving the tool something real to work from, not a pattern-matched guess.

Why does AI write requirements that look right but aren't?

It generates from patterns of what requirements usually look like, not your product's specific terminology and decisions. The output fits the format while missing the meaning. This is an AI context problem – the tool is writing from a generic understanding of requirements rather than your product's actual rules and terminology.

Can't I just review AI-generated requirements more carefully?

Review helps catch mistakes, but it doesn't fix the underlying issue: the AI is still generating from incomplete product context, so the same classes of errors keep reappearing. Fixing the input is more reliable than catching every bad output.

Is this an AI problem or a spec problem?

Both. Human-written specs drift from the product too; AI makes the same mistake faster and at scale.

What stops AI from generating almost-right specs?

Giving it your terminology and the decisions behind the work – structured Product Knowledge served through an MCP server – so it writes from the actual product instead of a guess.

How does AI requirements gathering go wrong?

Most AI requirements gathering fails because the tool has no access to product-specific context – it doesn't know your terminology, your past decisions, or your business rules. It fills those gaps with plausible language that looks right and isn't.