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@ddiorr26: She got this really fast 🤯next up…’paw’ #puppytraining #dogtricks #yorkshireterrier #puppiesoftiktok

ddiorr26
ddiorr26
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Region: GB
Thursday 07 May 2026 11:00:51 GMT
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Comments

reesezpiecez315
Reese :
So is it just my puppy that’s a spazz than
2026-06-07 00:41:14
2
toypoodlebiggie
ToyPoodleBiggie :
Aw what a clever girl 🥰🐾
2026-05-30 11:52:11
3
sharnae60
✨ Sharnae & Co. 🇯🇲 :
Soooo cute!! Good job Dior😍
2026-05-07 20:44:58
2
lifeofariiii
PrettyLeo :
😂😂😂
2026-08-28 00:25:44
0
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Large Language Models (LLMs) are powerful, but they don’t always know your latest documents or private data. Retrieval-Augmented Generation (RAG) solves this by retrieving relevant information before generating a response. 🔄 RAG WORKFLOW 📄 Documents ⬇️ ✂️ Text Chunking ⬇️ 🧠 Embedding Model ⬇️ 🗄️ Vector Database ⬇️ 🔍 User Query ⬇️ 🧠 Query Embedding ⬇️ 🎯 Similarity Search ⬇️ 📚 Retrieve Relevant Chunks ⬇️ 🤖 LLM + Retrieved Context ⬇️ 💬 Accurate Answer 🏗️ KEY COMPONENTS 1️⃣ Data Source 📄 Knowledge comes from: 📚 PDFs 🌐 Websites 📄 Documents 🗃️ Databases 📧 Emails 2️⃣ Text Chunking ✂️ Large documents are divided into smaller sections. This makes retrieval faster and more accurate. 3️⃣ Embedding Model 🧠 Converts text into numerical vectors that capture semantic meaning. Examples: 🤗 Sentence Transformers 🧠 OpenAI Embeddings 🌟 BGE Models 4️⃣ Vector Database 🗄️ Stores embeddings for fast similarity search. Popular options: 📦 Qdrant ⚡ Pinecone 🟣 Weaviate 🦜 Chroma 📂 FAISS 5️⃣ User Query 🔍 The user’s question is also converted into an embedding. 6️⃣ Similarity Search 🎯 The vector database finds the most relevant document chunks based on meaning, not just keywords. 7️⃣ LLM Generation 🤖 The retrieved context is combined with the user’s question. The LLM generates an answer grounded in the retrieved information. 🚀 WHY USE RAG? ✅ Reduces hallucinations ✅ Uses private or company knowledge ✅ Answers questions about up-to-date information ✅ Improves accuracy with relevant context 🛠️ POPULAR TOOLS 🐍 Python 🤗 Hugging Face 🔗 LangChain 🦙 LlamaIndex 🗄️ Qdrant 📂 FAISS ⚡ Pinecone 🤖 OpenAI / Open-Source LLMs 🌍 REAL-WORLD APPLICATIONS 💬 AI Customer Support 📄 PDF Chatbots 🏢 Enterprise Knowledge Assistants ⚖️ Legal Document Search 🏥 Medical Information Systems 🎓 Educational Assistants 💡 RAG doesn’t make an LLM smarter by changing its knowledge. It makes the LLM more reliable by providing the right information at the right time before it generates a response. #RAG #LLM #GenerativeAI #ArtificialIntelligence #creatorsearchinsights
Large Language Models (LLMs) are powerful, but they don’t always know your latest documents or private data. Retrieval-Augmented Generation (RAG) solves this by retrieving relevant information before generating a response. 🔄 RAG WORKFLOW 📄 Documents ⬇️ ✂️ Text Chunking ⬇️ 🧠 Embedding Model ⬇️ 🗄️ Vector Database ⬇️ 🔍 User Query ⬇️ 🧠 Query Embedding ⬇️ 🎯 Similarity Search ⬇️ 📚 Retrieve Relevant Chunks ⬇️ 🤖 LLM + Retrieved Context ⬇️ 💬 Accurate Answer 🏗️ KEY COMPONENTS 1️⃣ Data Source 📄 Knowledge comes from: 📚 PDFs 🌐 Websites 📄 Documents 🗃️ Databases 📧 Emails 2️⃣ Text Chunking ✂️ Large documents are divided into smaller sections. This makes retrieval faster and more accurate. 3️⃣ Embedding Model 🧠 Converts text into numerical vectors that capture semantic meaning. Examples: 🤗 Sentence Transformers 🧠 OpenAI Embeddings 🌟 BGE Models 4️⃣ Vector Database 🗄️ Stores embeddings for fast similarity search. Popular options: 📦 Qdrant ⚡ Pinecone 🟣 Weaviate 🦜 Chroma 📂 FAISS 5️⃣ User Query 🔍 The user’s question is also converted into an embedding. 6️⃣ Similarity Search 🎯 The vector database finds the most relevant document chunks based on meaning, not just keywords. 7️⃣ LLM Generation 🤖 The retrieved context is combined with the user’s question. The LLM generates an answer grounded in the retrieved information. 🚀 WHY USE RAG? ✅ Reduces hallucinations ✅ Uses private or company knowledge ✅ Answers questions about up-to-date information ✅ Improves accuracy with relevant context 🛠️ POPULAR TOOLS 🐍 Python 🤗 Hugging Face 🔗 LangChain 🦙 LlamaIndex 🗄️ Qdrant 📂 FAISS ⚡ Pinecone 🤖 OpenAI / Open-Source LLMs 🌍 REAL-WORLD APPLICATIONS 💬 AI Customer Support 📄 PDF Chatbots 🏢 Enterprise Knowledge Assistants ⚖️ Legal Document Search 🏥 Medical Information Systems 🎓 Educational Assistants 💡 RAG doesn’t make an LLM smarter by changing its knowledge. It makes the LLM more reliable by providing the right information at the right time before it generates a response. #RAG #LLM #GenerativeAI #ArtificialIntelligence #creatorsearchinsights

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