Episode 5: Feeding the Beast – Crafting Effective Prompt Content

(Narrator): Welcome to Chapter 5: Prompt Content. This is the first chapter in Part II of the book, focusing on core techniques for prompt engineering.
(Narrator): After understanding how LLMs work and how chat models are built, Chapter 5 dives into the building blocks of your prompt: the content.
(Narrator): You need to figure out what information to include in your prompt to help the LLM address the user's problem. The sources suggest thinking systematically about where to find this content, grabbing as much as possible initially, with the understanding that you'll refine it later.
(Narrator): The chapter introduces two main types of content: Static Content and Dynamic Content.
(Narrator): Static Content is information you prepare beforehand. It's used to structure and clarify the general problem or task. This can include explicit instructions, like telling the model to use Markdown or not use hyperlinks. It also includes implicit nudges and examples, often in the form of few-shot prompting. Few-shot examples are a powerful way to show the model the exact format, style, and even distribution of expected outputs, as LLMs have a compulsion to continue patterns.
(Narrator): Dynamic Content, on the other hand, is information that changes from user to user or over time. It's retrieved at the time of a request and provides details specific to the current user and their problem.
(Narrator): Managing dynamic content often involves thinking of it as a search problem. You need to find the most relevant information from potentially vast sources. The chapter discusses techniques for this, including lexical retrieval (based on word overlap like Jaccard similarity or TF*IDF) and neural retrieval (using embeddings to find conceptually similar text).
(Narrator): Once you retrieve context, you need to snippetize it. This means breaking it into relevant, bite-sized chunks suitable for the prompt and the model's context window. Snippets should be modular, natural within the document format, and brief. The concept of elastic snippets is introduced, allowing different lengths or versions of the same content to be included based on available space.
(Narrator): Along with snippetizing, you need to prioritize the retrieved content. Assigning priority tiers or scores helps determine which snippets are most important and should be included first in the prompt.
(Narrator): Another technique discussed for managing large amounts of dynamic content is summarization. For text too long for the context window, hierarchical summarization can be used, summarizing parts, then summarizing those summaries, and so on. The chapter also highlights the importance of specific summarization, focusing on details relevant to the task at hand, not just a general synopsis.
(Narrator): In essence, Chapter 5 is about gathering all the potential content for your prompt, understanding the difference between static and dynamic sources, and learning techniques for retrieving, breaking down (snippetizing), prioritizing, and sometimes summarizing that content.
(Narrator): The chapter concludes by noting that while having this content is essential, simply dumping it into a prompt isn't enough; it needs to be structured and organized effectively. That leads us to the next step... assembling the prompt.