If you’ve used ChatGPT, Claude, or Gemini for more than a few minutes, you’ve likely seen it: a fluent, confident answer that looks right—but isn’t. In AI, we call this a hallucination. It is not the model being mischievous; it is the model doing exactly what it was trained to do: predict the next likely word, not guarantee the next correct one.

The good news is you can dramatically reduce hallucinations with a mix of product features, prompt patterns, and process controls. The even better news: once you understand why they happen, the fixes start to feel obvious.

What exactly is an AI hallucination?

A hallucination is when a model produces content that is plausible but false or unsupported. It can range from minor inaccuracies (misstating a date) to critical errors (inventing a medical dosage).

Real examples you may have seen:

  • A chatbot invents a source or quote that does not exist.
  • A code assistant returns a function call from a library that was never published.
  • A business assistant confidently reports last quarter’s revenue with numbers that differ from the finance system.

Two key flavors to keep in mind:

  • Factuality errors: The model states something untrue.
  • Faithfulness errors: The model’s answer is not grounded in the provided context or sources.

Why do chatbots make things up?

Large language models are probability machines. They learn statistical patterns from vast text corpora and generate the most likely continuation given your prompt and the conversation history. That design has consequences.

Common causes:

  • Lack of grounding: Without access to your data or authoritative sources, the model fills gaps with pattern-based guesses.
  • Ambiguous or leading prompts: Vague requests or prompts that imply an answer nudge the model to speculate.
  • Out-of-domain questions: When you ask about topics far outside the model’s training distribution, it generalizes—and may invent.
  • Overconfident style: Many models are optimized to be helpful and concise, which can sound certain even when uncertain.
  • Tool failures: If browsing, search, or a retrieval system fails silently, the model often proceeds anyway.

For a succinct overview of why this happens and practical mitigations, see this recent deep-dive on reducing hallucinations in LLMs: Reducing hallucinations in large language models.

When hallucinations matter (and when they don’t)

Not every error is a crisis. The right framing is risk-based:

  • Low risk: Brainstorming headlines, drafting social posts, creative writing. Mild hallucinations are often tolerable or even useful.
  • Medium risk: Summarizing meeting notes, internal research digests, sales enablement. You want sources and quick verification.
  • High risk: Legal guidance, medical suggestions, financial reporting, compliance workflows. Require strict grounding, human review, and audit trails.

Real-world scenarios:

  • Marketing: A model drafts a customer case study but invents a client quote. Easily fixed with a source check.
  • Support: A chatbot recommends an incorrect configuration step. Consequence: escalations and churn. Solution: retrieval plus guarded actions.
  • Data analysis: An assistant misreads a CSV and invents averages. Solution: offload computation to a verified tool (SQL engine, notebook kernel) and have the model describe the steps, not do the math freehand.

How leading assistants address hallucinations

Popular systems now expose features that help you stay grounded:

  • ChatGPT: Browsing, code interpreter, and retrieval integrations. You can ask it to cite URLs and to explicitly say “I don’t know” when sources are unclear.
  • Claude: Strong at reading long documents and following instructions; supports tool use and system prompts that enforce guardrails like “cite from provided documents only.”
  • Gemini: Tight integration with Google search and Workspace; can generate summaries with links and leverage enterprise data connectors for grounding.

Tip: Turn on features that say things like citations, grounding, data connectors, browsing, or code execution. These offload facts and calculations to systems that can check themselves.

Practical techniques to reduce hallucinations

You do not need a PhD to cut hallucinations by half. Combine these tactics:

1) Write grounding-first prompts

  • Put your sources in the prompt: “Use only the provided snippets to answer. If the answer is not in the snippets, say ‘Not found.’”
  • Ask for uncertainty expression: “If confidence is low, list what is missing.”
  • Constrain the task: “Summarize the bullets below in 3 sentences. No external facts.”

2) Use retrieval-augmented generation (RAG)

  • Connect the model to your docs, wiki, knowledge base, or database.
  • Provide the retrieved chunks in the prompt and require citations.
  • Log every retrieved source for auditability.
  • Metrics to track: hit rate (did we retrieve relevant content?), faithfulness (does the answer stick to the retrieved text?).

3) Prefer tools over text for hard facts

  • Math: Use calculators, Python sandboxes, or SQL—then have the model narrate the steps.
  • Up-to-date info: Use search or APIs instead of asking the model to recall.
  • Internal systems: Wrap actions in safe tools (e.g., “get_customer_by_id”) and make the model call those tools, not invent data.

4) Add structure to outputs

  • Request schemas: “Return JSON with fields: answer, confidence, citations.”
  • Structured outputs are easier to validate, test, and diff over time.
  • Schemas also help downstream apps block answers that lack citations.

5) Self-check and committee patterns

  • Ask the model to produce an answer, then to critique its own answer against sources.
  • Run two different models (e.g., Claude and GPT) and compare; if they disagree, route to a human.
  • Use consistency checks: Re-ask the same question with paraphrase; divergence flags potential issues.

Measuring hallucinations in practice

If you cannot measure it, you cannot manage it. Set up lightweight evaluation:

  1. Build a small golden set of 50-200 questions with verified answers and sources.
  2. Run nightly or pre-release evals that score:
    • Factuality: Is the claim true?
    • Faithfulness: Is it supported by retrieved or provided content?
    • Citation quality: Are links valid and relevant?
  3. Track trends by prompt version, model version, and retrieval settings.

Useful signals:

  • Percentage of answers with at least one valid citation.
  • Rate of “I don’t know” when gold answer is truly absent.
  • Human review time per answer (should go down if hallucinations drop).

Governance: make it safe by design

Hallucinations become manageable when your workflow assumes they can happen:

  • Define risk tiers and gate higher tiers with human approval.
  • Keep audit logs: prompt, retrieved context, model version, outputs, reviewer.
  • Provide user-facing disclaimers and confidence indicators.
  • Offer a one-click report issue button so users can flag errors.
  • Train teams to use verify-then-trust: skim citations first, not after.

A quick troubleshooting playbook

When you see a made-up answer:

  • Check retrieval: Did it find relevant documents? If not, improve indexing, chunking, and query rewriting.
  • Tighten the prompt: Add “Answer only from the provided sources.”
  • Add a tool: Offload data lookups or calculations.
  • Compare models: If two disagree, send to review.
  • Log and learn: Add the failure to your golden set.

Actionable next steps

Do these this week:

  • Implement grounding: Connect your assistant to a small, high-quality corpus (top 50 help articles) and require citations. Track citation presence in logs.
  • Add evals: Create a 100-question golden set and run nightly factuality/faithfulness checks. Fail the build if metrics regress.
  • Harden prompts: Update templates to include “If not in sources, say ‘Not found’.” Add a JSON schema with fields for answer, confidence, and citations.

Do these this quarter:

  • Introduce tool use for math, search, and internal data lookups.
  • Pilot a two-model check (e.g., ChatGPT and Claude) for high-risk flows, with human-in-the-loop for disagreements.
  • Establish governance: risk tiers, audit logging, and a review dashboard for flagged outputs.

Hallucinations are a feature of how language models think, not a bug in your workflow. With grounding, tools, structure, and steady evaluation, you can channel their fluency into reliable performance—so your team ships faster without shipping fiction.