How to integrate AI into your Knowledge Base
This is a high level advanced guide for maintainers of knowledge bases with some high level pointers on how to start effectively using AI to enhance curation.
Tip 1: No fancy frameworks needed - just simple MCPs or command line tools
Intimidated by the growing number of agent frameworks? Don't worry, you don't need most of these.
All you really need are a handful of simple MCPs servers or command line tools. These can be hooked up to generic frameworks.
The command line tools could be wrapped in a Docker container; this is the strategy for ODK-AI.
You should rely on existing tools for doing things like literature search - you only need to write MCPs that are specific to read/write/validation on your KB.
This is much easier if you follow O3 guidelines and manage your content in GitHub. In fact if your content is small enough, you might not need any new tools!
Tip 2: Keep AI instructions checked in at the root of your GitHub repo
Examples:
Tip 3: Train curators to use simple tool-enabled AI applications (e.g Goose)
Many AI hosts such as Claude Code or various VS code plugins are suboptimal for non-technical users. AI applications such as Claude Desktop may be better, but currently it's hard to configure.
At the time of writing, we recommend Goose as an AI app/host, due to these features:
- Ease of configuring MCPs
- Choice of either Desktop version (for non-devs) or Command Line (for devs)
- Ability to use multiple models including proxies.
See the Installation Guide
As an example, this video shows how to configure:
Set up GitHub actions
See some of the actions in this org. Again this works best if your content is managed according to O3 guidelines.