Vectorless RAG
Skip embeddings entirely. Build a tree index from document structure, summarize nodes with an LLM, and use structured output to find the answer. RAG without a single vector.
Every RAG tutorial starts the same way: chunk your document, embed the chunks, store them in a vector database. But open any earnings report and look at the table of contents. The document already tells you where everything is. Headers, sections, subsections - a built-in index that we flatten into vectors and throw away.
Vectorless RAG keeps that structure. Parse the document's hierarchy into a tree, summarize each node with an LLM, and at query time the model reads the tree index - titles and summaries, no content - and reasons about which nodes contain the answer. No embeddings, no vector math, no database.
What You'll Build
- Parse markdown into a hierarchical tree structure
- Generate bottom-up LLM summaries for each node
- Use structured output for reasoning-based retrieval
- Build a complete RAG pipeline without any embeddings
How It Works
Members onlyJoin 900+ members
Become a member for full access
“Best educational investment in my ML/AI journey.”
Ana Clara MedeirosAI Developer
Renews yearly at the price you joined at. Cancel anytime.
30-day money-back guarantee. One email, full refund. Yearly includes live cohort access.
What you get
6 modules and 42 hands-on lessons with runnable code, failure diagnosis, and measured comparisons
2 capstone repositories you build, test, evaluate, and keep as portfolio proof
Videos, source code, complete projects and architecture diagrams
Recordings of live sessions
Discord community access
Not ready? Try these free lessons first
Instant access after paymentSecure checkout via Stripe
