Prompting Fundamentals and How to Apply them Effectively

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Writing good prompts is the most straightforward way to get value out of large language models (LLMs). However, it’s important to understand the fundamentals even as we apply advanced techniques and prompt optimization tools. For example, there’s more to Chain-of-Thought (CoT) than simply adding “think step by step”. Here, we’ll discuss some prompting fundamentals to help you get the most out of LLMs. Aside: We should know by now that, before doing any major prompt engineering, we need reliable evals. Without evals, how would we measure improvements or regressions? Here’s my usual workflow: (i) manually label ~100 eval examples, (ii) write initial prompt, (iii) run eval, and iterate on prompt and evals, (iv) eval on held-out test set before deployment. Here are write-ups on practical evals for key tasks and how to build evals with a case study. We’ll use the Claude Messages API for the prompt and code examples below. The prompts are deliberately kept simple and can be further optimized

Prompting Fundamentals and How to Apply them Effectively eugeneyan Start Here Writing Speaking Prototyping About Prompting Fundamentals and How to Apply them Effectively [ llm production 🔥 ] · 17 min read Writing good prompts is the most straightforward way to get value out of large language models (LLMs). However, it’s important to understand the fundamentals even as we apply advanced techniques and prompt optimization tools. For example, there’s more to Chain-of-Thought (CoT) than simply adding “think step by step”. Here, we’ll discuss some prompting fundamentals to help you get the most ou

Prompting Fundamentals and How to Apply them Effectively

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