RAG
RAG Basic: End-to-End RAG
Build a complete RAG system on a fully local, open-source stack, FastAPI, Docling, OpenSearch, vLLM. Your documents never leave your machine.
You shipA local RAG system over your own docs
Build real-world AI/ML products deployed in production. Guided by a mentor at every step.
Day one, your mentor drafts a structured path with you: what to learn, in what order, what to build next. Then it reshapes as you progress and your goals change.
Each course ships you something real: a RAG system over your own docs, a fine-tuned model on real evals. Every AI/ML course takes you through deployment too, so you finish with something running in production, not a notebook. Start free with Python & SQL.
Build a complete RAG system on a fully local, open-source stack, FastAPI, Docling, OpenSearch, vLLM. Your documents never leave your machine.
Past the naive baseline, hybrid retrieval, rerankers, query rewriting, and evals that hold up once real users hit it.
When fine-tuning is actually the right answer. LoRA, full FT, and evals you can defend, not vibes.
Production-fluent Python for AI work, run right in the browser via Pyodide. From print() to an LLM cost reporter.
Window functions, joins, and the queries you'll actually write to pull training data.
After every lesson, we probe the exact concept tied to the role you're chasing. Nail it, skip ahead. Miss it twice, the lesson loops back differently. Every example pulls from the job you're transitioning into, not the average learner's.
Most AI tutors forget you between sessions. Your LaunchFar mentor remembers your background, the questions you've struggled with, and the roadmap you're walking, across months.
Every lesson generates practice questions tied to your weak spots. The mentor surfaces them again days later, until you genuinely have it, not just because you saw it once.
Not a newsletter. The briefing knows your roadmap and your level, so it surfaces papers, releases, and threads that actually matter to what you're building this week.
Every interactive lesson runs Pyodide live in your tab. Edit, run, debug, no environment setup, no Colab tokens to manage. Just code and feedback.
The mentor reads the AI internet for you. Papers, blog posts, GitHub releases, ranked by what's actually useful given where you are on your roadmap, not what's trending.
Most platforms grow by lowering the bar. We grow by holding it. These four are non-negotiable.
I built LaunchFar because I kept watching strong engineers stall on the jump into AI/ML, not for lack of skill, but for lack of someone who'd already walked the path and remembered where they were yesterday. So I'm starting as the first mentor myself: one that knows your stack, your goals, and the next move you should make.
Starting small on purpose, one verified mentor at a time.
A mentor that remembers you, courses that ship to production, a roadmap that adapts. The first 250 to join the waitlist get our first course, free.