Shivacode-37/ai-engineering-learning-lab

A compact collection of hands‑on Python experiments for building and evaluating AI engineering patterns around large language models. It focuses on prototyping retrieval‑augmented generation (RAG), embeddings, prompt/chain design with LangChain, and simple AI agent workflows. Intended for engineers, researchers, and learners who want runnable examples and patterns to explore LLM behavior, retrieval integration, and agent orchestration. Key features: example scripts and notebooks, demonstrative RAG pipelines, embedding and similarity experiments, agent/control-flow prototypes, and notes on environment and dependencies to reproduce experiments.

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