LLM-TLDR: Stop Feeding Your AI Raw Code

Large Language Models (LLMs) are revolutionizing the way we write and understand code. However, feeding large amounts of raw code to an LLM can be slow and expensive. What if there was a better way? Enter llm-tldr.

llm-tldr is an open-source tool that optimizes the way LLMs interact with codebases. Instead of feeding the model raw code, llm-tldr extracts the code's structure and dependencies, providing the LLM with a "tldr" (too long; didn't read) version of the code. This results in massive token savings and significantly faster queries.

What is LLM-TLDR?

llm-tldr is a tool that analyzes your codebase and builds a set of analysis layers, including an Abstract Syntax Tree (AST), a call graph, a control flow graph, a data flow graph, and a program dependence graph. It also creates a semantic index of your code.

This structured representation of your code is then fed to the LLM, giving it a deep understanding of your codebase without having to process every single line of code. The results are impressive: llm-tldr boasts up to 95% token savings and 155x faster queries.

Key Features of LLM-TLDR

llm-tldr is a powerful tool with a number of key features.

1. Drastic Token Savings

By providing the LLM with a summarized, structured representation of your code, llm-tldr can reduce the number of tokens required to process a query by up to 95%. This can lead to significant cost savings, especially when working with large codebases.

2. Lightning-Fast Queries

Because the LLM is working with a much smaller, more structured representation of the code, queries are processed much faster. llm-tldr has been shown to be up to 155x faster than feeding raw code to an LLM.

3. Deep Code Understanding

llm-tldr's analysis layers provide the LLM with a deep understanding of your code's structure and dependencies. This allows the LLM to answer more complex queries and provide more accurate and helpful suggestions.

4. Natural Language Search

With llm-tldr, you can search your codebase using natural language. For example, you could ask "where is the function that handles user authentication?" and llm-tldr will be able to find it for you.

Use Cases

llm-tldr is a versatile tool with a wide range of applications. Here are a few examples of how you can use it to improve your development workflow:

1. Codebase Exploration

llm-tldr is a great tool for exploring new codebases. You can use it to quickly get a high-level overview of the code and then drill down into the parts that are most relevant to your work.

2. Debugging

When you're trying to fix a bug, llm-tldr can help you to quickly find the code that is causing the problem. You can use natural language to describe the bug and llm-tldr will help you to pinpoint the source of the problem.

3. Code Generation

llm-tldr can be used to provide an LLM with the context it needs to generate new code. For example, you could ask the LLM to "add a new endpoint to the API that returns a list of users" and llm-tldr will provide the LLM with the information it needs to do it.

4. Improving AI Coding Assistants

By providing AI coding assistants with a structured understanding of your code, llm-tldr can help them to provide more accurate and helpful suggestions. This can make you a more effective and productive programmer.

Getting Started with LLM-TLDR

llm-tldr is still under active development, but you can check out the GitHub repository to learn more about it and see how it works.

GitHub: https://github.com/parcadei/llm-tldr

Conclusion

llm-tldr is a promising new tool that has the potential to revolutionize the way we interact with codebases. By providing LLMs with a structured understanding of our code, llm-tldr can help us to work faster, more efficiently, and more effectively.

As LLMs continue to become more powerful and more integrated into our development workflows, tools like llm-tldr will become increasingly important. Keep an eye on this project - it's one to watch.

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