In Context Learning

Flashcard: how does Reflexion let an agent learn from failed runs?

Flashcard: what is few shot prompting and what does the 'few' actually refer to?

Three concrete situations where fine-tuning is the wrong tool

Explain why in context learning in a RAG pipeline is functionally equivalent to approximate Bayesian inference even though no parameters are updated, citing the relevant theoretical results.

When an LLM in a RAG pipeline behaves differently because of the retrieved context, what is actually changing about the model?

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How does DSPy reframe prompt engineering and what does it gain over hand written prompts?

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