Our first post in this series provided an overview of how large language models (LLMs) work, the benefits and limitations of these models, and why the quality of their output depends on the quality of the prompts you give them. We also explained why evaluating and deciding how to apply LLM-powered tools requires a degree of AI literacy, or at least a fundamental understanding of how LLMs work.

In part two, we offer tips for getting the most from LLMs by providing sufficient context with prompts.

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