The gap between an AI feature that demos well and an AI feature that survives production is wider than almost anyone expects. We have shipped AI generation systems across multiple products this year — curriculum generation, assessment creation, content pipelines — and the failures we encountered were almost never the ones the demo suggested we should worry about.
Here is what actually breaks, and what fixes it.
Truncation is the silent killer
Large language models generate until they hit a token limit, and when they hit it mid-response, they do not fail loudly. They return a response that looks complete, parses as text, and is missing its final third. If your system expects structured output — JSON, a document with defined sections, a curriculum with a set number of units — truncation produces output that is subtly, catastrophically wrong. The user does not see an error. They see a curriculum missing its last three weeks.
The fix is layered. First, generous token budgets — we doubled ours after watching real usage, because real users request things demo users never do. Second, a repair parser: a deterministic layer that detects truncated structures and either completes them from context or re-requests only the missing section. Never assume the model finished. Verify it.
Identical requests should never cost twice
In any generation system with real users, requests repeat. Different families request the same grade-level curriculum. Different teams request the same report format. Naive systems regenerate every time — paying full inference cost, adding full latency, and introducing variance where users expect consistency.
A generation cache changes the economics completely. Hash the meaningful parameters of the request, store the validated output, and serve repeat requests instantly at zero inference cost. The second family to request a fourth-grade science curriculum gets it in milliseconds instead of a minute. The one rule that matters: anything personalized or containing user data bypasses the cache entirely. Cache the commodity, generate the personal.
Placeholders will find their way to production
Every AI system starts life with stub generation — placeholder logic that returns plausible output so the rest of the system can be built. The dangerous property of placeholder AI output is that it looks real. Unlike a missing image or a broken link, fake generated content passes casual review. We now treat placeholder generation paths as release blockers with the same severity as failing tests, because we have seen how easily "temporary" generation logic survives into a shipped build.
The boring engineering is the product
None of this is glamorous. Token budget tuning, repair parsers, cache invalidation rules, placeholder audits — no one demos these. But they are the difference between an AI feature and an AI product. The model provides the capability. The engineering around it provides the reliability. Customers experience the reliability.
This is the kind of system we build at Impartial AI Tech — AI that holds up after the demo ends. If your organization is trying to get AI generation into production, the contact form at drakepaulsen.com goes directly to me.