検索拡張生成 (RAG)
Retrieval-Augmented Generation (RAG) は、生成プロセス中に外部の知識源を統合することにより、Large Language Models (LLM) の精度と信頼性を高める AI フレームワークです。
RAGシステムは、情報検索とテキスト生成の強みを組み合わせます。まず、ユーザーのクエリに基づいて、外部の知識ベースから関連するドキュメントまたはデータを取得します。次に、この取得した情報でLLMの入力を補強し、モデルがより情報に基づいた、文脈的に正確な応答を生成できるようにします。このアプローチは、古くなったり不完全である可能性のあるLLMの内部知識への依存を減らし、生成された出力の全体的な品質と信頼性を向上させます。RAGは、最新の情報や専門知識を必要とするタスクに特に役立ちます。
graph LR
Center["検索拡張生成 (RAG)"]:::main
Pre_large_language_model["large-language-model"]:::pre --> Center
click Pre_large_language_model "/terms/large-language-model"
Pre_vector_database["vector-database"]:::pre --> Center
click Pre_vector_database "/terms/vector-database"
Pre_semantic_search["semantic-search"]:::pre --> Center
click Pre_semantic_search "/terms/semantic-search"
Center --> Child_rag_pipeline["rag-pipeline"]:::child
click Child_rag_pipeline "/terms/rag-pipeline"
Center --> Child_context_window["context-window"]:::child
click Child_context_window "/terms/context-window"
Rel_prompt_engineering["prompt-engineering"]:::related -.-> Center
click Rel_prompt_engineering "/terms/prompt-engineering"
Rel_generative_ai_agents["generative-ai-agents"]:::related -.-> Center
click Rel_generative_ai_agents "/terms/generative-ai-agents"
Rel_rlhf["rlhf"]:::related -.-> Center
click Rel_rlhf "/terms/rlhf"
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🧠 理解度チェック
🧒 5歳でもわかるように説明
🌍 Imagine you're taking a test. A regular AI tries to answer from memory alone. An AI using [RAG](/ja/terms/rag) is allowed to look at an open book (the [database](/ja/terms/database)) specifically related to the question before it writes down the answer. This makes it much less likely to make things up.
🤓 Expert Deep Dive
RAG architectures fundamentally shift LLM interaction from pure parametric recall to a hybrid parametric-retrieval paradigm. The core challenge lies in optimizing the retrieval-augmentation loop for relevance and efficiency. Techniques like dense passage retrieval (DPR) using bi-encoders, or hybrid search combining keyword and semantic matching, are crucial. Advanced RAG implementations explore iterative retrieval, query decomposition for complex questions, and re-ranking mechanisms to refine retrieved context. The choice of vector [database](/ja/terms/vector-database), embedding model, chunking strategy, and retrieval parameters (e.g., top-k) significantly impacts performance. Potential vulnerabilities include retrieval poisoning, where malicious data injected into the knowledge base can lead to biased or incorrect LLM outputs. Furthermore, the computational overhead of retrieval can introduce latency, a critical trade-off for real-time applications. Evaluating RAG systems requires metrics beyond standard LLM benchmarks, focusing on retrieval precision/recall and the factual consistency of the generated output with the retrieved context.