{"id":778,"date":"2025-06-10T10:53:33","date_gmt":"2025-06-10T09:53:33","guid":{"rendered":"https:\/\/www.methodix.co.uk\/?p=778"},"modified":"2025-06-10T10:53:33","modified_gmt":"2025-06-10T09:53:33","slug":"hallucination-isnt-a-bug-but-it-is-a-problem","status":"publish","type":"post","link":"https:\/\/www.methodix.co.uk\/index.php\/2025\/06\/10\/hallucination-isnt-a-bug-but-it-is-a-problem\/","title":{"rendered":"Hallucination isn’t a bug (but it is a problem)"},"content":{"rendered":"\n
\"\"<\/figure><\/div>\n\n\n

Part 2 of our \u201cBuzzword Breakdown\u201d series<\/strong>
This week, Sky News caught ChatGPT confidently delivering a transcript for a show\u2026 that it didn\u2019t have access to: https:\/\/news.sky.com\/video\/did-chatgpt-lie-to-sam-coates-about-transcript-for-podcast-13380234.<\/a>

\ud83d\udc68 \u201cHas this been uploaded?\u201d
\ud83d\udde8\ufe0f Yes.
\ud83d\udc68 \u201cIs it synthetic?\u201d
\ud83d\udde8\ufe0f No.
\ud83d\udc68 \u201cAre you sure?\u201d
\ud83d\udde8\ufe0f Yes. (pause) Okay, yes. I made it up.

This wasn\u2019t lying.
It\u2019s what the industry calls a hallucination.

\ud83e\udd16 What is a hallucination?<\/strong>
It\u2019s when an AI gives you an answer that sounds perfectly reasonable\u2014but isn\u2019t true.
These models don\u2019t deliberately mislead.
They\u2019ve just been trained to produce what sounds like the right answer\u2014not what is the right answer.

\ud83e\udde0 Why does this happen?<\/strong>
Because AI models don\u2019t \u201cknow\u201d things.
They don\u2019t have memory or understanding the way humans do.
If they don\u2019t have access to the right data, they fill in the blanks with their best guess<\/em>.

\u2705 So how do we stop it?<\/strong>
You can\u2019t eliminate hallucinations completely\u2014but you can design systems that catch and contain them. Here\u2019s how:

\ud83d\udcbe Give the model trusted information to work from<\/strong>
Pull answers from a live database or approved source material, not just the model\u2019s training data.
\ud83d\udde8\ufe0f Ask for the basis of an answer<\/strong>
Prompts like \u201cWhat\u2019s your source?\u201d or \u201cHow confident are you?\u201d encourage more cautious, transparent responses.
\ud83d\udc41\ufe0f\u200d\ud83d\udde8\ufe0f Ask the model to double-check its own output<\/strong>
With the right prompt, it can reflect and revise its initial answer.
\u2696\ufe0f Use a second model to review and flag risky content<\/strong>
Especially useful when accuracy really matters\u2014like legal, financial, or medical scenarios.
\ud83e\udd13 Design prompts that reward accuracy, not just fluency<\/strong>
Even small changes in phrasing can reduce the risk of confident nonsense.
\u26a1 Use model capabilities wisely<\/strong>
For high-stakes tasks, treat the AI as an assistant or idea-generator\u2014not the final authority.

At Methodix, this is the kind of design thinking we bring to every AI solution: practical, strategic, and human-aware.

Some hallucinations are obvious. Others? Convincing\u2014but still wrong.

That\u2019s why smart AI systems include a safety net\u2014catching the unicorns before they leave the factory. \ud83e\udd84<\/p>","protected":false},"excerpt":{"rendered":"

Part 2 of our \u201cBuzzword Breakdown\u201d seriesThis week, Sky News caught ChatGPT confidently delivering a transcript for a show\u2026 that 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2 of our \u201cBuzzword Breakdown\u201d seriesThis week, Sky News caught ChatGPT confidently delivering a transcript for a show\u2026 that 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