25. Learn to Recognize Hallucination Patterns in Your Domain
Hallucination — the model generating plausible-sounding content that isn't grounded in fact — is not random. It has patterns. Models hallucinate in predictable ways, in predictable situations, and the developers who work most effectively with agents have learned to recognize the patterns that are specific to their domain.
The general patterns are well documented. Models fabricate citations with the right structure but wrong details. They confuse entities that are similar in some dimension — same name, same field, same time period. They fill gaps in their knowledge with extrapolations that follow the logic of the domain but aren't actually true. They're more likely to hallucinate at the edges of their training data — niche topics, recent events, highly specialized domains where the training corpus was thin.
But the general patterns are less useful than the domain-specific ones. A developer working with a legal agent learns that the model reliably fabricates case citations — gets the court and the general area of law right, invents the case name and date. A developer working with a medical agent learns that the model tends to confuse similar drug names and misstate dosages in ways that follow pharmaceutical naming conventions. A developer working with a code-generation agent learns that the model confidently uses library functions that don't exist but probably should.
These patterns are learnable, but only through exposure. You have to run the agent on enough real tasks, catch enough specific failures, and build up a picture of where this model, on this task, in this domain, tends to go wrong. That knowledge doesn't transfer cleanly from model to model or domain to domain — it's acquired locally, per system.
The payoff is a targeted skepticism that's much more efficient than global distrust. Instead of verifying everything, you verify the things that are likely to be wrong. You build checks for the specific failure modes you've learned to expect. You know which parts of the output to read carefully and which parts you can trust.
General skepticism protects you from known hallucinations. Domain knowledge tells you where to look.