Скачать или смотреть RAG [ retrieval augmented generation ] : Secret to Trustworthy AI

Retrieval-Augmented Generation (RAG) is the single most crucial architecture for bringing Large Language Models (LLMs) into the enterprise. Why? Because RAG solves the fundamental problems of the LLM knowledge cutoff and disastrous hallucinations.
However, simple vector search (or "Naive RAG") often fails in production. We dive deep into the next generation of RAG architectures—RAG 2.0—and the advanced techniques needed to build accurate, trustworthy, and autonomous AI applications.

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What You Will Learn:
1. Why Basic RAG Fails in the Real World 💔
The process of retrieval is the hardest part of RAG. We break down the key failure points (FP) that break systems:
• FP2: Missed Top Ranked Documents: The answer exists, but retrieval ranks it too low to be seen by the LLM.
• FP4: Not Extracted: The correct answer is in the context, but the LLM ignores it due to noise or conflicting information.
• FP5 & FP6: Wrong Format or Incorrect Specificity: The LLM disregards instructions, failing to return answers as a table, list, or with the required level of detail.
2. Advanced RAG Architectures for Complex Queries 🚀
We explore how advanced techniques move beyond retrieving simple text chunks to handle complex reasoning:
• 2.1 GraphRAG (Structural Intelligence):
◦ This method extracts entities and relationships from documents, organizing them into a knowledge graph.
◦ It is essential for solving multi-hop questions that require reasoning over interconnected facts scattered across many documents.
◦ Real Example: GraphRAG is backed by Knowledge Graphs for better business AI systems.
• 2.2 Agentic RAG (Autonomous Reasoning):
◦ Agents use LLMs to automate reasoning and tool selection. The RAG pipeline becomes just one of the tools available.
◦ An agent can perform Query Planning, breaking complex multi-step questions (e.g., comparing documents or analyzing structured data) into executable sub-queries.
◦ This system addresses limitations like complex summarization, document comparison, and analyzing structured data which confuse traditional RAG.
• 2.3 Self-Correction RAG (Trust & Quality Control):
◦ Self-RAG trains the LLM to use reflection and critique tokens to dynamically assess whether external information is needed and critique its own generated response for factual accuracy.
◦ Corrective RAG (CRAG) uses an external evaluator to judge document quality and trigger a search tool (like a web search) if necessary, solving the "Missing Content" failure point.
3. Building Enterprise-Ready Systems 💼
• 3.1 Citation-Aware RAG: For high-stakes applications (legal, medical), RAG can be configured to return answers with fine-grained citations by storing spatial anchors (like page numbers and bounding boxes) in the metadata.
• 3.2 The Hybrid Approach: For optimal results, combining techniques is key. You can Fine-Tune the model for specific domain terminology and style (behavior) and use RAG for injecting real-time facts and proprietary knowledge.

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Don't miss: See how academic AI applications like Harvard's ChatLTV use RAG to clarify complex concepts and how platforms like Vimeo use RAG to enable conversations with video content.
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