Glossary
Large Language Model (LLM)
A large language model is a neural network trained on very large text corpora to predict language, and it is what produces the prose in ChatGPT, Gemini, Perplexity and Copilot. Left alone it answers from training data; connected to search or a document store it answers from retrieved sources instead.
In plain terms
The model writes the sentences. Whether those sentences mention you usually depends on what was retrieved and fed to it, not on the model itself.
Why it matters
Understanding the split between training knowledge and retrieved knowledge tells you which lever you can actually pull. You cannot retrain a frontier model; you can influence what it retrieves.
How to apply it
- Optimise for retrieval, since that is the part of the pipeline your content touches.
- Assume nothing about training cut-offs — verify with live prompts.
- Test the same prompt on several models; behaviour differs materially between them.
Example
A sales-enablement platform tests the same product question against GPT-4, Gemini and Claude and finds each gives a different, sometimes outdated description of its own pricing tiers — evidence that the models are answering from training data rather than a live, retrieved page.
Related terms
Back to the full glossary (75 terms).
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