Episode 6: The Puzzle – Constructing the Perfect Prompt

(Narrator): Welcome to Chapter 6: Assembling the Prompt. Building on Chapter 5, where we discussed gathering content, this chapter focuses on putting those pieces together.
(Narrator): Chapter 6 is all about taking the potential content you've gathered – both static and dynamic – and crafting a prompt that effectively communicates your needs to the LLM. The goal is to elicit relevant, coherent, and contextually accurate responses.
(Narrator): The chapter guides you through the process of shaping your prompt. This involves exploring different structures and options for organizing your content snippets. How you organize these pieces is crucial for the prompt's effectiveness.
(Narrator): A core concept is the Anatomy of the Ideal Prompt. Prompts should generally be concise and crisp for effectiveness, using less computational power, and importantly, staying within the hard cut-off of the context window size. The anatomy includes elements like an introduction, a refocus that clarifies details, and a transition to signal the model should start generating the solution.
(Narrator): The prompt and its completion together form a document, and the Little Red Riding Hood principle from Chapter 4 is revisited here. You should make your prompt resemble document types similar to those in the LLM's training data for predictable and stable completions.
(Narrator): The sources highlight several common document types to aim for:
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The Advice Conversation: This mimics a dialogue where one participant (application or user) asks for help, and the model acts as the help provider. You can use various formats like freeform text, script, markerless, or structured. With chat models using ChatML, the document format is largely decided for you, structured as a conversation transcript.
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The Analytic Report: This type follows a formal structure with introductions, expositions, analyses, and conclusions. It's good for tasks where objective analysis is common. Using Markdown is recommended for reports due to its universality and clear structure. Reports allow for structured sections like Scope or Ideas/Analysis before a Conclusion, aiding chain-of-thought prompting.
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The Structured Document: These follow a formal specification, like XML, YAML, or JSON, making complex outputs easier to parse. Examples include Anthropic's Artifacts prompt using XML to delineate interaction pieces. OpenAI's efforts to make models generate accurate JSON is noted, especially for their tools API.
(Narrator): Formatting your collected content into Prompt Elements is key. These snippets should be modular (easy to add/remove), natural within the chosen document format, and brief. Few-shot examples can be formatted explicitly or integrated naturally into the document as solved tasks. Including "asides" or comments can provide background context without requiring the model to use it in a specific way.
(Narrator): The concept of Elastic Snippets addresses situations where a piece of content could be included in different lengths or forms. This allows for flexibility when managing the prompt's token budget. You might have multiple versions (short to long) of an element or create incompatible elements representing the same content in different ways.
(Narrator): To effectively assemble the prompt, you need to manage relationships between elements. This includes their order (position), importance (how crucial they are), and dependencies (requirements or incompatibilities).
(Narrator): The final assembly process is framed as an optimization problem. You must decide which elements to include to maximize value, while respecting dependency structures and staying within the prompt length or token budget constraint.
(Narrator): In conclusion, Chapter 6 covers how to choose the right document format, convert information into well-formatted prompt elements, manage relationships between them, and assemble the final prompt as the successful completion of the feedforward pass.
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