AI agent projects commonly fail at the boundary between model knowledge and engineering: teams know LLM APIs but struggle to turn them into robust RAG/Agent products. This repo fills that gap by combining conceptual notes, runnable examples, two end-to-end projects, and an interview-oriented question bank so practitioners can move from “calling an API” to shipping production-ready agent systems.
What Sets It Apart
- End-to-end, runnable focus: includes runnable examples and two completed projects (an NL2SQL e-commerce Q&A pipeline and a multi-agent deep-research system), so readers can follow from idea to deployable prototype.
- Engineering-first orientation: emphasizes Python + LangChain/LangGraph pipelines, tool/function calling, memory, MCP/A2A protocols, hybrid retrieval (vector + sparse + rerank) and deployment considerations (Docker, Ollama, vLLM).
- Career alignment: pairs technical chapters with an interview question bank and project framing guidance to help learners present work in hiring contexts.
Who It's For and Trade-offs
Great fit if you are an engineer or technical learner who wants a hands-on, Python-centric path to RAG and agent engineering, including production trade-offs (retrieval design, cost/observability, safety patterns). Look elsewhere if you need a vendor-agnostic theoretical survey, a Java-first stack, or a lightweight one-file tutorial—this repository is broad and assumes willingness to run multi-component projects and manage API keys or local model runtimes.
Where It Fits
Positions itself between short blog how-tos and paid bootcamps: more system-level and practice-oriented than scattered posts, but not a formal university course. Useful as a bootstrapped internal curriculum for teams adopting agent architectures or individuals preparing for AI agent engineering roles.