{"version":"1.0","type":"card","id":"44ce8d8c-70b8-4e4c-827b-50527ca60172","url":"https://stacklist.com/card/44ce8d8c-70b8-4e4c-827b-50527ca60172","title":"RAG: The Definitive Guide 2025 — Chitika","source_url":"https://www.chitika.com/retrieval-augmented-generation-rag-the-definitive-guide-2025/","note":"The most thorough 2025 guide to Retrieval-Augmented Generation — the primary technique for giving LLMs access to knowledge beyond their context window. Covers chunking, vector retrieval, reranking, and advanced variants like GraphRAG and HyDE.","image":{"url":"https://ucarecdn.com/cfbfcb53-cad6-4899-9a2a-d4946d432712/","alt":"RAG: The Definitive Guide 2025 — Chitika","width":1200,"height":675},"stack":{"id":"e16dcdcb-06b9-481c-aef9-40b6d73a5c8e","title":"Master AI & LLM Context: From Basics to Production","url":"https://stacklist.com/c/technology/stack/e16dcdcb-06b9-481c-aef9-40b6d73a5c8e"},"created_at":"2026-07-02T10:09:05.236Z","updated_at":null,"aco":{"summary":"Retrieval-Augmented Generation (RAG) is a paradigm that combines external knowledge retrieval with generative AI to produce accurate, context-aware responses grounded in real-time data. This guide covers RAG's technical foundations, evolution through 2025, practical applications in domains like legal research and healthcare, and the ethical considerations of this transformative technology.","tags":["retrieval-augmented-generation","rag","large-language-models","information-retrieval","generative-ai","semantic-search","adaptive-retrieval"],"key_entities":[{"name":"Retrieval-Augmented Generation (RAG)","type":"concept","confidence":1},{"name":"Dense Vector Representations","type":"concept","confidence":0.8},{"name":"Adaptive Retrieval Mechanisms","type":"concept","confidence":0.85},{"name":"Reinforcement Learning","type":"concept","confidence":0.7},{"name":"Knowledge Graphs","type":"concept","confidence":0.7},{"name":"Semantic Search","type":"concept","confidence":0.75},{"name":"Multi-Stage Retrieval","type":"concept","confidence":0.75},{"name":"arxiv.org","type":"organization","confidence":0.6}],"classification":"reference","language":"en","confidence":0.85,"provenance":{"model":"claude-opus-4-6","tool":"@stacklist/mcp-server@2.0.0","confidence":0.85,"timestamp":"2026-07-02T10:09:12.904Z"},"token_counts":{"approximate":12356,"cl100k":8307},"content_hash":"sha256:6bec526846bbfd842c1f8575bef46d67000a3273c1661204dc6d831d18baeb15","acp_version":"0.2","body_available":true,"body_tokens":12356,"visibility":"public","agent_accessible":true,"status":"final"},"_links":{"self":"/api/public/card/44ce8d8c-70b8-4e4c-827b-50527ca60172.json","html":"https://stacklist.com/card/44ce8d8c-70b8-4e4c-827b-50527ca60172","md":"/api/public/card/44ce8d8c-70b8-4e4c-827b-50527ca60172.md","stack_json":"/api/public/stack/e16dcdcb-06b9-481c-aef9-40b6d73a5c8e.json"}}