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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.

Not necessarily. While some programming experience is helpful, many of my clients have successfully transitioned from web development, data science, or other technical backgrounds. We'll assess your current skills during our strategy call and create a personalized plan that meets you where you are.

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.

Most clients invest 10-15 hours per week, but this can be flexible based on your schedule. We'll have weekly 1:1 calls plus time for you to work on projects and learning. The key is consistency. Regular, focused effort beats occasional marathons.

You become a member of the AI Native Engineer community, and you stay and keep going. Career transitions take different amounts of time for everyone, and I'm not going to abandon you if things take longer. You get ongoing support through good times and bad.

Online courses give you content. 1:1 coaching gives you a personalized roadmap, direct feedback on your work, career strategy, interview prep, and accountability. You get answers to your specific questions and guidance tailored to your unique situation instead of generic advice meant for everyone.

Investment details are discussed during the 30-minute strategy call, where we'll assess your goals and create a custom plan. The program is designed to pay for itself quickly through your increased salary. Most AI engineers see a 20-50% pay increase.

Absolutely. Most of my clients work full-time and make steady progress. We'll schedule calls at times that work for you and create a realistic plan that fits your schedule. Consistency matters more than intensity.