دانلود کتاب استفاده از RAG آسان شد – راهنمای کامل بصری برای RAG

Retrieval-Augmented Generation has become one of the most important patterns in applied AI. The idea is deceptively simple: instead of asking a language model to answer from memory, you first retrieve relevant information from your own documents and hand it to the model as context. The model reads, then answers. No hallucination. No guesswork. But that simple idea hides a landscape of complexity. How should you split documents into searchable pieces? What if the user’s question is vague or ambiguous? What if the retrieved passages are almost-but-not-quite relevant? What if the model ignores the context and hallucinates anyway? Each of these failure modes has spawned its own family of techniques, and the field is growing fast. This book maps that landscape. It covers 22 distinct RAG techniques, from the foundational pipeline through advanced strategies like graph-based retrieval and self-correcting systems. Each chapter extracts the algorithm and intuition from a real implementation, then teaches it without a single line of code. The following illustration shows how these techniques span the full RAG pipeline, from how you prepare documents to how you verify the final answer.