RAG
Production Agentic RAG
Build a multi-agent retrieval system that handles real data, real evals, and real failure modes. Ship a working agent over your own docs by the end of week three.
Not influencer advice. Not AI slop. Real operators who shipped what they're teaching you — vetted one by one, starting with the founder.
Open LinkedIn and someone two years into their career is telling you how to become staff. Open TikTok and a coach with a ring light is selling you a framework. Open Twitter and an anonymous account with 80k followers is telling you how to negotiate.
None of them will be there on Thursday when your stakeholder pushes back. None of them remember what you said three weeks ago. None of them have done the thing.
LaunchFar is the opposite of that. Every mentor is someone who has shipped the work. Every conversation is remembered. Every piece of advice sits inside a real relationship — not a feed.
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.
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.
Each course ships you something real — an agent in production, a fine-tuned model on real evals, an embeddings pipeline that holds up. Not toy notebooks.
Build a multi-agent retrieval system that handles real data, real evals, and real failure modes. Ship a working agent over your own docs by the end of week three.
When fine-tuning is actually the right answer. LoRA, full FT, evals you can defend.
Interactive Python in the browser via Pyodide. From zero to writing your first ML utility.
Window functions, joins, and the queries you'll actually write to pull training data.
From sentence-transformers to a vector DB you'd actually run. Cosine, eval, drift.
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 — 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 proposes the next move. You drag, reorder, or kill anything. The roadmap reshapes itself when your goals change — not a fixed curriculum you have to follow blind.
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.
A growing library of patterns that have shipped in real systems — chunking strategies, eval rubrics, prompt templates that survived contact with users. Copy, adapt, ship.
All tiers cancel anytime. Mentor is the entry point — most engineers start there.
Most platforms grow by lowering the bar. We grow by holding it. These four are non-negotiable.
I built LaunchFar because I kept watching brilliant builders stall on the jump into AI/ML — not for lack of skill, but for lack of someone who'd already walked the path and would remember where they were yesterday.
starting small on purpose — one verified mentor at a time.
Every engineer I worked with had the same question: 'How do I move into ML?' LaunchFar is the answer I wish someone had given me — a mentor that knows your stack, your goals, and the next move you should make.
for engineers who want to ship, not just to watch.
A mentor that remembers you, courses that ship to production, a roadmap that adapts. Cancel anytime. Liftoff in 60 seconds.