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A short true story to frame this topic: A lawyer once submitted a federal court brief that cited six earlier cases. The citations looked legitimate — page numbers, quotes, and case numbers — but when the judge checked the sources none of those cases existed. The lawyer had asked an AI chatbot to do his research; the chatbot had invented all six citations. This illustrates a core fact: the model wasn’t malfunctioning — it was doing exactly what it was designed to do. Understanding the mechanics behind that behavior makes clear why hallucinations never disappear entirely.

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How these models work (high level)

  • A pretrained language model is a frozen set of learned parameters (weights) captured when training ended. It does not contain a live database, search index, or an internal table of facts.
  • Its fundamental mechanism is next-token prediction: given a prompt, it repeatedly predicts the most likely next token to generate text.
  • That prediction loop has no built-in truth-checker or “no results found” gate. The base model generates continuations that look plausible based on patterns seen during training.
  • Many deployments add retrieval, tool use, or verification layers which alter behavior, but those are external systems built around the model — not part of the core prediction loop.
Example: a common question vs. a niche question
  • Common question:
    • Prompt: “Who wrote Romeo and Juliet?”
    • This appears frequently across books and guides, so the model reliably outputs: Shakespeare.
  • Niche question:
    • Prompt: “Is there a flag to disable telemetry in internal-tool?”
    • Training data likely contains little or nothing about this private tool. The model has seen many examples of command-line flags and will finish the pattern with something plausible (e.g., --no-telemetry) — formatted and confident but possibly fabricated.
The same base model and the same next-token loop produce both answers. Nothing in the model explicitly marks one answer as true and another as false — it completes patterns rather than verifies facts.
A man is writing on a transparent lightboard, sketching neon-colored boxes, arrows and labels like "Hallucinations," "MODEL," "PROMPT," and "Shakes." He appears to be illustrating a flow diagram about prompts and model behavior.
Concrete illustration (prompts and outputs)

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Key takeaways about hallucinations

  • The model is a plausibility machine: it predicts what comes next based on learned patterns, not by looking up facts.
  • Plausible does not equal true — fluent and confident text can still be incorrect.
  • Hallucinations are more likely for:
    • Niche or internal facts (private tools, internal APIs),
    • Specifics such as URLs, citations, quotes, and version numbers,
    • Information created after the model’s training cutoff.
  • Confidence or fluent tone is not a reliable indicator of correctness.

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Can we eliminate hallucinations?

  • You can reduce hallucinations but you cannot remove them completely.
  • Newer and better models tend to hallucinate less, but they are still error-prone.
  • Adding retrieval, grounding, or verification systems reduces risk: these components supply up-to-date, authoritative evidence that the model can cite.
  • There is no single “hallucination module” you can unplug — the same model that handles common facts will invent details on thin topics unless supported by external verification.

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Practical mitigations — checklist and techniques

Practical tips:
  • Validate any high-risk outputs (citations, URLs, legal/medical facts, software flags) with authoritative sources.
  • Prefer direct verification for things after the model’s cutoff date.
  • Treat model-generated specifics as drafts that require human validation.
Remember: prediction, not lookup. Most of the time plausible outputs are correct — which is why these models are useful — but the rare incorrect outputs are dangerous because they sound convincing. Always verify critical specifics.

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References and further reading

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