Audience
Data engine leads, infra leads, privacy reviewers
Chris Royse field notes
Stop Dumping Every Corpus Into One RAG Database
The swappable-brain pattern: isolated databases, provenance, cross-database search, and cleaner context for high-stakes work.
Agents / Channel video / 14:54
Core idea
A corpus should be a focused brain with its own source trail. The AI should switch context deliberately instead of swimming in one giant mixed database.
Focused Context
Watch on YouTube· 14:54
Stop Dumping Every Corpus Into One RAG Database
Labs evaluating private corpora need isolation, provenance, and clean retrieval before they can trust any downstream signal.
Watch videoOpen the full video on YouTubeWhat to take from it
The videos are raw build context. These notes translate them into the shortest useful frame for creators, companies, and AI lab readers.
Keep projects isolated unless comparison is intentional.
Preserve page-level and document-level provenance.
Search across corpora only when the question requires it.
Continue this thread.
Related notes stay inside the same problem area first, then move to the next useful context.
Watch + read / 21:21
Context Engineering Starts With The Desired Output
Define the current state, define the target state, use memory to strengthen the path, then verify the connection.
Watch + read / 14:41
Multi-Agent Search For Hard Research Questions
A multi-agent search workflow can explore large question spaces, collect evidence, and build research artifacts faster.
Watch + read / 6:48
AI Memory Without Hallucinations Is Mostly Boundaries
A memory system needs isolated stores, explicit provenance, query expansion, source reading, and refusal when the corpus does not answer.
Make it concrete.
Send the audience, data type, target task, proof bar, and sharing limits.