Source URL: https://simonwillison.net/2025/Apr/14/llms-as-the-first-line-of-support/
Source: Simon Willison’s Weblog
Title: Using LLMs as the first line of support in Open Source
Feedly Summary: Using LLMs as the first line of support in Open Source
From reading the title I was nervous that this might involve automating the initial response to a user support query in an issue tracker with an LLM, but Carlton Gibson has better taste than that.
The open contribution model engendered by GitHub — where anonymous (to the project) users can create issues, and comments, which are almost always extractive support requests — results in an effective denial-of-service attack against maintainers. […]
For anonymous users, who really just want help almost all the time, the pattern I’m settling on is to facilitate them getting their answer from their LLM of choice. […] we can generate a file that we offer users to download, then we tell the user to pass this to (say) Claude with a simple prompt for their question.
This resonates with the concept proposed by llms.txt – making LLM-friendly context files available for different projects.
My simonw/docs-for-llms contains my own early experiment with this: I’m running a build script to create LLM-friendly concatenated documentation for several of my projects, and my llm-docs plugin (described here) can then be used to ask questions of that documentation.
It’s possible to pre-populate the Claude UI with a prompt by linking to https://claude.ai/new?q={PLACE_HOLDER}, but it looks like there’s quite a short length limit on how much text can be passed that way. It would be neat if you could pass a URL to a larger document instead.
ChatGPT also supports https://chatgpt.com/?q=your-prompt-here (again with a short length limit) and directly executes the prompt rather than waiting for you to edit it first(!)
Via @carlton
Tags: open-source, llms, ai, generative-ai, carlton-gibson, chatgpt, claude
AI Summary and Description: Yes
Summary: The text discusses utilizing large language models (LLMs) to enhance user support in open-source projects, proposing a method to streamline assistance for anonymous users by creating LLM-friendly context files. This approach addresses the challenges of information overload faced by maintainers, aligning with contemporary trends in AI and generative AI security.
Detailed Description:
The content focuses on the integration of large language models in the context of user support for open-source projects. Key points include:
– **User Support Dynamics**: The traditional model of user support in open-source environments often leads to overwhelming demand from anonymous users who pose support requests.
– **Utilization of LLMs**: The author proposes leveraging LLMs like Claude and ChatGPT to handle initial user queries more efficiently. Instead of direct interaction through issue trackers, users would be guided to use LLMs by providing them with context-specific files.
– **Generating LLM-Friendly Documentation**: The author references a project (simonw/docs-for-llms) that aims to compile and generate documentation tailored for LLM interaction to facilitate better user inquiries.
– **Technical Limitations**: The text notes challenges, such as the short text length limit when passing prompts through specific URLs, indicating a need for advancements in how LLMs can handle larger sets of data or longer queries.
– **Open Contribution in GitHub Context**: The ongoing struggle against the “denial-of-service attack” mentality from anonymous requests emphasizes a critical need for solutions that can automate and streamline responses without overwhelming maintainers.
– **Related Concepts**: The reference to llms.txt indicates a broader movement towards creating compatible resources for LLMs across various projects and platforms.
This text is relevant for professionals in AI and open-source security, illustrating innovative applications of generative AI in support frameworks and highlighting both opportunities and challenges in making this integration effective.