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Friday 09 October 2026 21:39:32 GMT
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Nhìn lại hành trình bên nhau 4 năm thật không hề dễ dàng. Chắc chắn yêu nhau không thể tránh khỏi những lúc cãi vã, giận hờn. Nhưng mong chúng ta hiện tại và tương lại vẫn mãi luôn bên cạnh nhau.❤️#xuhuong #abcxyz
Nhìn lại hành trình bên nhau 4 năm thật không hề dễ dàng. Chắc chắn yêu nhau không thể tránh khỏi những lúc cãi vã, giận hờn. Nhưng mong chúng ta hiện tại và tương lại vẫn mãi luôn bên cạnh nhau.❤️#xuhuong #abcxyz
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Most RAG diagrams show everything happening at once. But production RAG is really a two-part game. 🎮🤖 PART 1️⃣ — Build the world before users arrive Level 0 is the offline indexing pipeline: 📚 Documents → ✂️ Chunk the content → 🔢 Create embeddings → 🗄️ Store everything in the Vector DB This level runs before the live request. Once the index is ready, the agent unlocks the online workflow. PART 2️⃣ — Answer one live request 🔍 Level 1: Retrieve + Augment The user submits a question, and the agent queries the existing Vector DB. The important dependency is: Vector DB → Retrieve Retrieve collects the most relevant evidence. Augment then re-ranks that evidence and packages it into focused context. 🌈 Level 2: Generate + Verify Claude receives the original question plus the assembled context and generates the answer. Before reaching the finish flag, the response is verified: ✅ Is it grounded in the retrieved evidence? ✅ Does it answer the actual question? ✅ Can it include useful citations? ✅ Did the workflow stay within its guardrails? Only then is the answer ready. 🏁 The mental model: Build the index once. Retrieve for every request. Generate from evidence. Verify before responding. Agentic RAG is more than connecting a vector database to an LLM. The agent coordinates retrieval, memory, tools, context construction, generation, and verification—while deciding what should happen next. Every completed stage unlocks the next level. 🚀 Which AI concept should Rainbow Claw’d explain next? 🌈 #AgenticRAG #RAG #AIAgents #LLM #ClaudeAI
Most RAG diagrams show everything happening at once. But production RAG is really a two-part game. 🎮🤖 PART 1️⃣ — Build the world before users arrive Level 0 is the offline indexing pipeline: 📚 Documents → ✂️ Chunk the content → 🔢 Create embeddings → 🗄️ Store everything in the Vector DB This level runs before the live request. Once the index is ready, the agent unlocks the online workflow. PART 2️⃣ — Answer one live request 🔍 Level 1: Retrieve + Augment The user submits a question, and the agent queries the existing Vector DB. The important dependency is: Vector DB → Retrieve Retrieve collects the most relevant evidence. Augment then re-ranks that evidence and packages it into focused context. 🌈 Level 2: Generate + Verify Claude receives the original question plus the assembled context and generates the answer. Before reaching the finish flag, the response is verified: ✅ Is it grounded in the retrieved evidence? ✅ Does it answer the actual question? ✅ Can it include useful citations? ✅ Did the workflow stay within its guardrails? Only then is the answer ready. 🏁 The mental model: Build the index once. Retrieve for every request. Generate from evidence. Verify before responding. Agentic RAG is more than connecting a vector database to an LLM. The agent coordinates retrieval, memory, tools, context construction, generation, and verification—while deciding what should happen next. Every completed stage unlocks the next level. 🚀 Which AI concept should Rainbow Claw’d explain next? 🌈 #AgenticRAG #RAG #AIAgents #LLM #ClaudeAI
Bí kíp lấy lòng mẹ chồng cho các nàng dâu đây ạ #HạtNêmBestFoods #NgọtThanhTômThịt #QuánXuyếnVừaVíVừaÝ #HợptáccùngUnilever
Bí kíp lấy lòng mẹ chồng cho các nàng dâu đây ạ #HạtNêmBestFoods #NgọtThanhTômThịt #QuánXuyếnVừaVíVừaÝ #HợptáccùngUnilever


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