Learn RAG Systems
Land AI Jobs.

RAG (Retrieval-Augmented Generation) combines LLMs with external knowledge retrieval. Instead of relying solely on what the model was trained on, RAG systems fetch relevant documents and use them to generate accurate, grounded responses. In 2026, RAG is the foundation of most enterprise AI applications: chatbots, search, document Q&A, knowledge bases. Companies need engineers who can build RAG systems that actually work in production, not just demo well.

Extremely. RAG appears in 70%+ of AI engineering job postings in 2026. Why? Every company wants to build AI products using their own data, and RAG is how you do it. Skills like chunking strategies, embedding model selection, vector database optimization, and RAG evaluation are specifically called out in job requirements. If you can build and debug production RAG systems, you're ahead of most candidates.

You should be comfortable with Python and have basic familiarity with APIs. Understanding how LLMs work at a conceptual level helps but isn't strictly required. You don't need a PhD or deep ML knowledge. Most RAG engineering is about software engineering skills: data pipelines, system design, debugging, and optimization. If you can write clean Python code and learn new libraries, you can learn RAG.

With focused effort and good guidance, 8-12 weeks. The core concepts (embeddings, chunking, retrieval) take 2-3 weeks. Building your first production-quality system takes another 3-4 weeks. The remaining time goes into advanced topics (hybrid search, reranking, evaluation) and building portfolio projects that demonstrate your skills to employers. Self-study takes longer; coaching accelerates the timeline significantly.

Start with Chroma or pgvector for learning. They're simple to set up and teach the core concepts. For production knowledge, learn Pinecone (managed, scales easily) or Weaviate (open source, feature-rich). The specific database matters less than understanding the concepts: indexing algorithms (HNSW, IVF), similarity metrics, metadata filtering. Once you understand one well, switching is straightforward.

Build projects that show production thinking, not just tutorials: 1) A document Q&A system with evaluation metrics (shows you can measure quality), 2) A multi-source RAG system combining different document types (shows real-world complexity), 3) A RAG system with hybrid search and reranking (shows advanced skills). Include documentation explaining your chunking strategy, embedding choice, and how you handled edge cases. This is what hiring managers want to see.

It probably isn't, and you should hold the money. Most cohort dropouts are people who couldn't articulate what they were shipping when they signed up. That's why the consult exists, and why I turn down most applications. If we get on the call and you can't tell me what you'll have shipped at the end of week 8, I'll point you to the AI Native Engineer community until you can.

Yes, the cohort works for people shipping their first serious AI system whether the goal is to land a senior role or to launch a product. The shipped system serves both equally well.

You need to be able to code in Python or TypeScript. Complete beginners can follow the classroom they get access to before the cohort sessions to come in well-prepared.

You'll spend 3 hours every Tuesday in the live session and roughly 3 hours of async work in between, for 8 weeks. The Tuesday session time is fixed.

It's a four-figure investment that we discuss during the 30-minute consult, alongside whether the cohort is the right fit for your project.

Yes, most attendees do. The live session is one Tuesday a week and the async work fits around your existing schedule, as long as you can carve out roughly 6 hours a week.