Build a knowledge bot
on your real documents.
A 4-week hands-on cohort for intermediate engineers. Pinecone + Claude + LlamaIndex. By week 4, you've shipped a RAG system over YOUR real docs — citation-faithful answers, evals wired up, deployable to production.
Capstone-required. The certificate gets issued after a Nolola partner reviews your working system — including the eval suite. We'll fail you if citations aren't actually grounded.
Engineers stuck at
“works in the notebook.”
Cohort structure exists because most self-taught RAG attempts die at the eval gap. This one finishes with a shipped system.
- You've built a RAG demo that hallucinated in week two and you don't know why
- You can read TypeScript or Python and have shipped to production before
- You want to build a knowledge bot over your real docs, not a toy index over Wikipedia
- You've read 15 blog posts on chunking strategy and they all contradict
- You want production-grade — evals, observability, cost control — not a notebook
Never called the Anthropic API? Start with the free Prompt Engineering Basics first. Want the RAG built FOR you? See the productized RAG Chatbot.
A real RAG system.
Not a notebook.
Everything in your repo, on your infra, answering from your real documents.
A working RAG on YOUR docs
Not a toy index over Wikipedia. Your SOPs, your support content, your product docs — whatever you scope in week 1.
Citation-faithful answers
Every answer cites the source paragraph it came from. The retrieval-first design means hallucinations get caught at evaluation, not by an angry user.
Evaluation harness
Groundedness + faithfulness + answer-quality evals wired up. You see when the bot drifts and what caused it — before it ships, after every update.
Production-deployable code
Everything in your repo, on your infra. No Nolola lock-in. Ready to integrate into your chat UI, Slack bot, or product surface.
Week by week.
Ends in production.
Document strategy
Why most RAG fails before retrieval. Source quality, chunking strategy, what to ingest and what to skip. Decide what your capstone bot is going to know.
Embeddings + vector index
Pick an embedding model. Build a working Pinecone or Cloudflare Vectorize index. Wire ingestion that updates without rebuilding the world.
Retrieval + reranking
Top-k tuning, hybrid search (BM25 + dense), cross-encoder reranking, citation-preserving retrieval. Where most accuracy is won or lost.
Answer generation + capstone
Claude prompts for citation-faithful answers, evals (groundedness, faithfulness), cost guardrails, deploy the capstone. Partner review + certification.
Learn the stack,
or buy the build.
RAG Systems for Business
- Deliverable
- Working RAG over your real docs + Nolola-verified cert
- Right fit
- You want to ship a production RAG system, not write a notebook
Shipped in production.
Citations intact.
“I'd been stuck on the same hallucination problem for two months. Week 3 reranking lesson solved it in one session. The bot now answers from our SOPs accurately — citations included — and the support team trusts it.”
“Capstone was a customer-facing FAQ bot over our help docs. Week 4 we deployed it to staging, week 6 it was live, deflecting 25% of tier-1 tickets two months later. The eval framework is what made it shippable.”
“I came in having read the LlamaIndex tutorial and built a notebook demo. Left with a real production system, observability wired, and the confidence to defend the architecture in a code review.”
Common questions
Intermediate. You should be able to read TypeScript or Python and have shipped something to production before. If you've never called the Anthropic API or worked with a vector DB, the free Prompt Engineering Basics + a week of LlamaIndex docs are a better starting point.
These are the production-stable defaults we've shipped RAG systems on for SMB clients. The principles transfer to other stacks (Cloudflare Vectorize, Weaviate, OpenAI embeddings, raw Anthropic without LlamaIndex) — we cover when to swap and why. By the end you can architect a RAG system from first principles on any stack.
Yes — that's the point. The capstone is a working RAG over real documents you pick in week 1. If you have NDA or security concerns, we've helped students work in a sanitized subset or on synthetic-but-realistic data. Reach out before enrolling if you want to scope this together.
A working RAG system that answers questions from your real docs with citations, has an eval suite (groundedness + faithfulness scored), and is deployable to production. 30-min code review with a Nolola partner before certification. We'll fail you if the citations aren't actually grounded — that's what makes the cert mean something.
Two differences. (1) Production focus: tutorials get you to a working notebook; this gets you to a deployable system with evals and cost guardrails. (2) Document strategy: week 1 spends real time on what to ingest and how to chunk — the part most tutorials skip and where most RAGs fail. Tutorials are great for syntax; this is for shipping.
~4 to 6 hours: 1 live session (90 min), 2-3 hours on assignments, 1 hour Slack + peer review. Week 4 is heavier as the capstone comes together — closer to 8 hours.
RAG can spiral if you don't watch embeddings + retrieval calls. Week 4 covers cost guardrails: caching strategy, retrieval budget per query, when to use cheaper models in the chain. Most capstones end up at $5-50/month to run, depending on volume.
Top-performing certified grads get an invitation to apply for the Nolola AI Talent Marketplace, where companies actively hire AI builders for paid engagements. Not auto-listed — you go through expert vetting — but the certificate puts you in the funnel.