10-Part Series
A hands-on walk from Python fundamentals to deploying production LLM systems. Each part builds on the last, ending with a self-hosted, fine-tuned, quantized, RAG-augmented AI service you've trained and deployed yourself.
Type hints, async, context managers, and tests — the Python that holds up under production AI load. Build a CLI tool you'll extend across every part.
From four lines of OpenAI SDK to a production-grade client wrapper with retries, streaming, structured output, and cost tracking.
Wrapping the LLM client in a production-ready FastAPI service: validation, streaming, auth, rate limiting, and a Dockerfile.
How embeddings work, which model to pick, and how to add semantic search to the FastAPI service from Part 3.
Chunking, hybrid retrieval, prompt construction, citations, and evaluation — the parts of RAG that actually matter.
QLoRA + Unsloth on a free Colab T4. Dataset prep, hyperparameters, training, evaluation — end to end.
14 GB FP16 down to 4 GB AWQ with sub-1% accuracy loss. Full pipeline from merged checkpoint to AWQ + GGUF, with benchmarks.
From AWQ checkpoint to a production endpoint. Multi-LoRA hot-swapping, prefix caching, and swapping the local endpoint into the Part 5 RAG.
Distil a fine-tuned 4B teacher into a 1.5B student via synthetic data — with quality-control filters and an honest eval against the teacher.
The series finale: concurrency patterns, caching, structured logging, monitoring, graceful degradation, and a one-page shipping checklist.