What Is Hallucination Detection?

What Is Hallucination Detection?

Hallucination detection is the process of catching LLM or agent outputs that state unsupported, contradicted, or fabricated claims. It is an AI reliability failure-mode control used in eval pipelines, production traces, RAG systems, and agent workflows. The detector compares the answer with retrieved context, reference data, tool results, or policy facts, then flags claims that should not be trusted. FutureAGI anchors this workflow on eval:DetectHallucination, with failures routed to alerts, guardrails, or regression datasets.

Why It Matters in Production LLM and Agent Systems

Hallucination detection matters because the highest-risk wrong answer often looks polished. A support agent says a refund window is 60 days when policy says 30. A legal RAG assistant cites a clause that is not in the contract. A data analyst agent invents a column name, then writes a query around it. Those failures do not always throw exceptions. They ship as confident prose.

The pain lands on different teams at once. Developers lose time debugging prompts that only fail on edge cases. SREs see normal latency and token metrics, but rising thumbs-down rates. Product teams see trust erosion after users act on a false claim. Compliance teams need evidence showing whether the model used approved sources or improvised.

Agentic systems make this more severe in 2026 pipelines. A single hallucinated planner step can trigger a wrong tool call, a misleading recovery step, and a final answer that explains the failed trajectory as if it were intentional. In logs, the symptoms are subtle: low groundedness on answer spans, retrieval chunks that do not contain the asserted fact, repeated tool retries, and user corrections that mention “that is not in our docs.” Without detection at the span level, teams only notice after the unsupported claim becomes an incident.

How FutureAGI Handles Hallucination Detection

FutureAGI’s approach is to treat hallucination detection as both an eval and a runtime control. The anchor surface is eval:DetectHallucination, exposed as the DetectHallucination evaluator. It detects hallucinated or unsupported claims in model output. Teams pair it with HallucinationScore for a continuous trend and Groundedness when the answer must stay inside retrieved context.

A typical workflow starts with a RAG support assistant instrumented through traceAI-langchain. The retrieval span captures the documents used for the answer, while the answer span carries the model output and trace metadata such as model, route, and prompt version. FutureAGI runs DetectHallucination on the output plus context and writes pass/fail, reason, and evaluator name back to the trace. The dashboard then tracks hallucination-fail-rate by route and prompt version.

When the fail rate crosses a threshold, the engineer has concrete next actions. They can open the failing examples, inspect whether retrieval or generation caused the issue, add the cases to a regression eval, and attach an Agent Command Center post-guardrail that blocks unsupported claims before the answer reaches the user. For higher-risk workflows, the same detector can route to a fallback response that asks for clarification or cites only verified context.

Unlike Ragas faithfulness, which is mainly a RAG claim-support metric, FutureAGI can apply the same detection pattern to final answers, agent reasoning steps, tool summaries, and structured extraction outputs.

How to Measure or Detect It

Wire hallucination detection as a measured production signal, not a manual review queue:

• fi.evals.DetectHallucination — pass/fail evaluator for hallucinated or unsupported claims in the output.
• fi.evals.HallucinationScore — continuous score for trend lines, release gates, and severity bands.
• fi.evals.Groundedness — context-bound check for whether the answer stays inside retrieved evidence.
• Dashboard signal: hallucination-fail-rate-by-cohort — split by model, prompt version, route, dataset, and customer tier.
• User-feedback proxy: correction-rate — count thumbs-down events or tickets where the user says the answer is factually wrong.

from fi.evals import DetectHallucination

evaluator = DetectHallucination()
result = evaluator.evaluate(
    output="Refunds are available for 60 days.",
    context="Refund requests must be filed within 30 days."
)
print(result.score, result.reason)

Good detection needs evidence. For RAG, pass retrieved chunks. For workflow agents, score planner summaries, tool outputs, and final responses separately. For policy assistants, keep reference policy text versioned so regressions compare against the exact document users relied on.

Common Mistakes

• Scoring only the final answer. Agent hallucinations often start in the planner or a tool summary, long before the final response.
• Treating retrieval failure as generation failure. Pair DetectHallucination with ContextRelevance so you know whether context was missing or ignored.
• Using one threshold across domains. A medical assistant, sales assistant, and code helper need different fail-rate budgets.
• Letting the generator judge itself. Same-family judging under-reports unsupported claims, especially on subtle date, citation, and numeric errors.
• Ignoring unsupported but harmless claims. Low-severity unsupported claims are still drift signals; trend them before they become release blockers.