What hallucination risk means
Hallucination risk is not just a model property. It is a use-case property shaped by task ambiguity, grounding quality, guardrails, domain stakes, review paths, and how wrong answers are detected.
Grounding strategies and failure modes
RAG, web search, fine-tuning, and hybrid grounding reduce different risks, but none eliminate hallucination. Retrieval can fetch the wrong passage. Search can find stale sources. Fine-tuning can encode outdated behaviour. Guardrails need measurable tests.
What to test before shipping
Test adversarial prompts, missing context, conflicting sources, outdated documents, domain edge cases, and escalation behaviour. A risk estimate is only useful if it turns into a pre-launch eval set and a production monitoring plan.
Assumptions and methodology
Risk is scored from task ambiguity, grounding strength, guardrail quality, and domain severity, then mapped to low, medium, high, or unacceptable.
The page keeps the interaction fast, accessible, and dependency-light while preserving the important planning behaviour from the implementation plan: editable inputs, visible outputs, no signup gate, structured data, internal links, citations, and clear caveats. Heavy runtime features such as Monaco, solc, exact tokenizers, or PDF export should remain code-split when added so the public page stays fast.
Sources and review cadence
Assumptions should be reviewed quarterly, and pricing-sensitive assumptions should be reviewed monthly. The public data files in the repo include last-reviewed or last-verified dates where the plan calls for them.
Need the architecture behind the number?
Use this tool to narrow the conversation, then bring the scenario into an architecture review if the decision affects budget, security, compliance, or production reliability.
Book a strategy callFrequently asked questions
What is the difference between hallucination and ungrounded generation?
Hallucination is a false or unsupported output presented as fact. Ungrounded generation is output produced without an explicit source. Ungrounded output can be acceptable for brainstorming but risky for factual, regulated, financial, legal, medical, or operational decisions.
Does RAG eliminate hallucinations?
No. RAG reduces risk by giving the model relevant context, but retrieval can fail, sources can conflict, and the model can still misread evidence. RAG needs citation checks, evals, and fallback behaviour.
Do LLM judges work?
LLM judges can catch some quality issues and scale evaluation, but they are not neutral truth machines. Use them with rubrics, sampled human review, deterministic checks, and domain-specific failure cases.
What is unacceptable risk?
Unacceptable risk means the use case can create serious harm and lacks enough grounding, guardrails, or human approval. Safety-critical, regulated-high, and autonomous decisioning workflows can fall into this band quickly.
Are reasoning models less prone to hallucination?
Reasoning models can improve multi-step consistency, but they can still produce unsupported claims. Better reasoning does not replace grounding, source verification, or domain evaluation.
What is the cheapest mitigation that works?
The cheapest useful mitigation is often a constrained output format plus a mandatory source or confidence field and escalation below a threshold. It will not solve every risk, but it makes failures easier to detect.