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Rewrite the Cookbook introduction to make it more accessible #2163
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Pull Request Overview
This PR modernizes the introductory documentation for LLMs by replacing the outdated how_to_work_with_large_language_models.md
with a new developer-focused guide openai-cookbook-llms-101.md
. The new article provides a practical, code-first introduction to LLMs with hands-on examples in both Python and JavaScript.
- Removes verbose, less practical content and replaces it with concise, actionable guidance
- Adds comprehensive code examples and streaming implementations
- Includes practical tips on parameters, limitations, and common pitfalls
Reviewed Changes
Copilot reviewed 4 out of 4 changed files in this pull request and generated 5 comments.
File | Description |
---|---|
registry.yaml | Adds entry for the new LLMs 101 article with metadata |
authors.yaml | Adds new author "paytonison" with profile information |
articles/openai-cookbook-llms-101.md | New comprehensive, code-focused LLM introduction guide |
articles/how_to_work_with_large_language_models.md | Removes the old introductory article |
Tip: Customize your code reviews with copilot-instructions.md. Create the file or learn how to get started.
Co-authored-by: Copilot <[email protected]>
Co-authored-by: Copilot <[email protected]>
This pull request replaces the old introductory article on large language models with a new, more concise and practical guide aimed at developers. The new article,
openai-cookbook-llms-101.md
, provides hands-on code examples, covers core concepts, and highlights practical tips for working with LLMs in real applications.Documentation update and improvement:
how_to_work_with_large_language_models.md
article, which was more verbose and less focused on code-first guidance.openai-cookbook-llms-101.md
, that delivers a practical, code-centric introduction to LLMs, including Python and JavaScript examples, quick-start guides, and explanations of key concepts like tokenization, embeddings, transformer layers, and sampling.Developer-oriented enhancements:
Motivation
The original introductory article felt slapdash and more like something written by a person who’s long been working with AI, throwing together a random assortment of very basic things without clearly explaining the finer points of what an LLM really is and how they’ve evolved into so much more. It’s also more casual.
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