I've been indexing everything I read into Nuveki's RAG system and it's become incredibly powerful. Here's my setup.
What I Index
- Articles & blog posts — save as PDF, upload to Library
- Meeting notes — copy from Google Docs, paste into a Library item
- Research papers — upload PDFs directly
- Personal notes — anything I write that I might want to reference later
- Email threads — important decision threads, saved as text files
Organization
I use Library folders to keep things organized:
/research/ai — AI and ML papers/research/product — product strategy articles/work/meetings — meeting notes by month/work/decisions — key decision docs/personal/learning — course notes, tutorials
How I Use It
Cross-Document Questions
"What have I read about retrieval-augmented generation in the past month?"
RAG pulls from multiple indexed documents and synthesizes an answer with citations.
Decision Support
"Based on the meeting notes from this quarter, what were the main objections to the pricing change?"
Instead of re-reading 12 meeting notes, the agent finds the relevant passages instantly.
Writing with Context
"Write a project proposal for X. Use my library docs about Y and Z as background context."
The agent weaves in relevant information from my knowledge base naturally.
Tips
- Index regularly — make it a habit. I spend 5 minutes at the end of each day uploading anything noteworthy.
- Use descriptive titles — RAG search benefits from clear titles and summaries.
- Don't over-organize — the whole point of RAG is that you don't need perfect organization. The search handles discovery.
- Tag consistently — a few well-chosen tags go a long way for filtering.
Six months of documents indexed and I genuinely can't imagine going back.