Reducing AI Hallucinations With Better Context

A hallucination is a confident answer that is not supported by fact. Inside a company, the most common cause is simple: the model was not given the facts it needed, so it produced the most plausible text instead. Better context gives the model the right facts, with sources, so it does not have to guess and its answers can be checked.

• Missing facts: the answer is in a system the model cannot reach.
• Stale facts: retrieval returns an old version of a policy or price.
• Conflicting facts: two sources disagree, and nothing says which one is authoritative.
• Unlinked facts: the model gets a document but not the customer, owner or decision it belongs to.
• Noise: too much loosely related text hides the one passage that matters.

• Connect more sources, with permissions, so the facts are reachable.
• Record when each fact was true, and prefer current facts.
• Record each fact's source and authority, so conflicts can be resolved.
• Link facts in a context graph, so retrieval returns related facts, not just similar text.
• Return fewer, more relevant facts. This is the core of context engineering.

When every fact in the context has a source, the model can cite it and a reviewer can check it. An answer with no supporting source is a signal to stop and verify. This matters most for AI agents, which act on their answers.

See how East River Labs grounds AI in sourced company context, or talk to us.