Path 01
Ship a RAG Product for Your Own Data
The complete path for a production RAG feature — foundations, retrieval quality, prompt structure, evaluation, and monitoring.
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Five technical tracks covering the skills engineers and architects need to build LLM-powered systems that actually work in production — not just in demos.
The concepts every LLM practitioner needs before writing a single prompt.
Zero-shot to few-shot, chain-of-thought, and structured output patterns.
Chunking, embeddings, vector search, and fixing hallucinations in production.
When to fine-tune vs. prompt, LoRA, QLoRA, evaluation, and deployment.
Planning loops, tool use, memory, and multi-agent coordination.
The complete path for a production RAG feature — foundations, retrieval quality, prompt structure, evaluation, and monitoring.
The architectural decision, done properly — cost, quality, maintenance, and the model context that decides the answer.
From agent loop to observability, guardrails, and the governance stance production actually requires.
The prompt-side, serving-side, model-side, and vendor-side levers that move real cost and latency numbers.