Keeping documentation and SDK updates aligned with evolving "LLM contexts" can quickly overwhelm dev teams. At VideoDB, we've built an open-source solution—Agent Toolkit—that automates syncing your docs, SDK versions, and examples, making your dev content effortlessly consumable by Cursor, Claude AI, and other agents. Ready-to-use template available.
This touches on a critical issue I've encountered in AI development: the synchronization between documentation and rapidly evolving AI systems.
Here are my key learnings:
1. Version Control for Context: I've found that treating context as a first-class citizen in version control is crucial. Each model iteration should have its context version tracked alongside code changes.
2. Bidirectional Traceability: In my experience, implementing bidirectional links between documentation and code/model behavior helps catch context drift early. I use a MECE framework to ensure completeness.
3. Automated Validation: I've implemented hooks that verify documentation consistency with model behavior during CI/CD. This caught several instances where model updates silently broke assumptions in the docs.
The challenge isn't just keeping docs in sync, but preserving the why behind decisions across model iterations.
Here are my key learnings:
1. Version Control for Context: I've found that treating context as a first-class citizen in version control is crucial. Each model iteration should have its context version tracked alongside code changes.
2. Bidirectional Traceability: In my experience, implementing bidirectional links between documentation and code/model behavior helps catch context drift early. I use a MECE framework to ensure completeness.
3. Automated Validation: I've implemented hooks that verify documentation consistency with model behavior during CI/CD. This caught several instances where model updates silently broke assumptions in the docs.
The challenge isn't just keeping docs in sync, but preserving the why behind decisions across model iterations.