AI / LLM Basics Interview Questions
Define In-Context Learning?
In-context learning is a model's ability to pick up a new pattern or task just from examples given directly in the prompt, without any additional training or parameter updates.
- The model isn't actually "learning" in the traditional sense, its weights don't change
- Instead, it recognizes the pattern demonstrated in the prompt and continues it for the new input
- Works because the model's pretraining exposed it to an enormous variety of patterns and structures it can recognize and mimic
This capability is what makes prompting techniques like few-shot examples effective, you're not training the model, you're showing it exactly what kind of response you want within the conversation itself.
Do a model's weights change during in-context learning?
What makes in-context learning possible in the first place?
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