LLM App Builder
The 'AI engineer' path — code an LLM app end to end
You'll have shipped an LLM-powered app with RAG and streaming, know where each piece fits, and speak the language fluently.
Comfortable with basic programming (Python or JS). You want to build real AI apps, not just use them.
- Write and run basic Python.
- Comfortable with a terminal and git.
Ship a 'chat with X' app (your notes, a PDF library, a codebase) — streaming, RAG-grounded, cited, deployed.
- 1
Stage 1: Python you'll actually use
The minimum viable Python for AI work — the rest you'll pick up on demand.
- Course: Python for AI (Gentle Start)
- Interview: NumPy vectorization (Python)
- Interview: Pandas groupby (Python)
Deliverable · Load a CSV with pandas, filter it, and print a summary in under 20 lines. - 2
Stage 2: Talk to a model from code
Your first real AI code — this is the atomic unit of every LLM app you'll ever build.
- Tutorial: Call an LLM API in Python
- Tutorial: Stream LLM responses for a snappy UX
- Interview: What is a token (LLM fundamentals)
Deliverable · Ship a 50-line CLI that streams answers from an LLM about anything you paste in. - 3
Stage 3: Give it memory
Every chat, every agent, every assistant needs conversation state. It's simpler than it sounds.
Deliverable · Extend your CLI into a persistent chatbot with a running conversation history. - 4
Stage 4: Embeddings & semantic search
The math behind RAG, recommendations, and 'similar to X' — used everywhere.
- Tutorial: An intro to embeddings
- Interview: Embeddings & semantic search (Mid)
- Interview: Cosine vs dot vs Euclidean
Deliverable · Embed 50 short docs. Given a query, return the 5 most similar. No LLM yet. - 5
Stage 5: RAG: ground the model in your data
The single most useful pattern in production AI. Everything else is a variation on this loop.
- Course: Building LLM Apps with RAG
- Tutorial: Build a minimal RAG system
- Interview: Chunking (RAG)
- Interview: Debug a RAG system (Senior)
Deliverable · Turn your chatbot into a RAG bot over a set of docs you actually care about. - 6
Stage 6: Reliability & evaluation
The difference between a demo and a product. Measure quality, then improve it on purpose.
- Tutorial: Five ways to reduce hallucinations
- Interview: Evaluate a RAG system (Senior)
- Blog: Evaluation is the moat
Deliverable · Build a 20-question eval set for your RAG bot. Fix the top 3 failure modes. - 7
Stage 7: Ship it
A thing that lives on the internet with your name on it is worth 100 side projects that don't.
- Blog: AI stack for 2026
- Interview: Design a RAG chatbot (System design)
- Interview: Latency vs cost tradeoffs (Mid)
Deliverable · Deploy your RAG app publicly. Write a post about what you learned. Link it in your bio.