@20lz: اخذيني من الهلاك بوسط حضن الامان #dancewithpubgm #explore #فلاح_المسردي #فيحان_المسردي

᷂عبدالرحمن ᷂العازمي

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Wednesday 03 June 2026 22:43:23 GMT

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rabiuu7 :

ايييه يافلاح 💔

2026-06-04 23:36:20

3

musxe.bale.go :

hy

2026-08-19 06:00:29

0

محمدابراهيم عيد براك رشايده :

الله يافلاح

2026-06-13 21:10:59

0

.. :

أنا أقبلت

2026-06-28 22:34:45

0

A :

يسلالاام

2026-06-04 06:23:29

2

AM :

فلااحح ي فلااااح 💔

2026-06-07 03:42:24

0

مَ. :

2026-06-05 18:58:51

1

MNASEEM :

beautiful words

2026-06-05 12:40:07

1

صدقه جاريه للمرحوم سعود عبدلله :

2026-06-12 12:08:25

0

𓅓 :

2026-06-05 17:29:09

0

. :

2018💔💔🥀

2026-06-04 19:58:15

0

hxmkd :

رد رز

2026-06-04 11:40:21

0

م̀́ها :

2026-06-04 20:49:45

1

ABCD123456 :

2026-06-04 17:29:16

2

ابو خالد الهذلي :

🤣🤣🤣🤣🤣😃😃

2026-06-09 11:20:05

1

احمد الحاج الراشدي :

💔💔💔

2026-06-04 13:22:13

2

صدام صالح_Sadam :

🥰🥰🥰

2026-06-05 15:25:08

1

الشاعر بو شرّين الشمراني 📝 :

🥺

2026-06-04 04:36:24

2

أبـــو قحـــــــــــــاز :

🥰🥰🥰

2026-06-03 22:45:05

1

محمدابراهيم عيد براك رشايده :

🥰🥰🥰🥰🥰🥰

2026-06-13 21:10:37

0

مسعود حماد :

🥰🥰🥰

2026-06-25 19:30:51

0

H🇸🇦 :

👌👌👌🥰🥰🥰🥺🥺🥺

2026-06-04 14:18:40

0

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Large Language Models are powerful, but they can struggle when they need information that isn’t in their training data or when answers require access to private, current, or specialized knowledge. That’s where RAG comes in. 🔍 WHAT IS RAG? RAG combines two main components: 🔎 Retriever → Finds relevant information 🤖 Generator → Uses that information to generate an answer Instead of asking an LLM to answer from memory alone, RAG gives it relevant context first. 🔄 HOW RAG WORKS 📄 Documents ⬇️ ✂️ Chunking Break documents into smaller pieces. ⬇️ 🧠 Embeddings Convert text into numerical representations. ⬇️ 🗄️ Vector Database Store the embeddings for efficient retrieval. ⬇️ ❓ User Query The user asks a question. ⬇️ 🔎 Retriever Find the most relevant chunks. ⬇️ 📚 Context Relevant information is added to the prompt. ⬇️ 🤖 LLM / Generator Generates a response using the retrieved context. ⬇️ 💬 Final Answer 🧩 THE TWO CORE PARTS 1️⃣ RETRIEVER 🔎 Its job is to find the most relevant information. Common techniques: 🔹 Vector Search 🔹 Keyword Search 🔹 Hybrid Search 🔹 Reranking 2️⃣ GENERATOR 🤖 Usually an LLM that takes: User Question + Retrieved Context and produces the final response. 🚀 WHY USE RAG? ✅ Use private documents ✅ Access updated information ✅ Ground answers in source material ✅ Reduce unsupported answers ✅ Build domain-specific AI assistants 🌍 REAL-WORLD APPLICATIONS 📚 Chat with PDFs 🏢 Enterprise Knowledge Bases 🎓 Educational Assistants ⚖️ Legal Document Search 🏥 Healthcare Information Systems 💬 Customer Support 📝 Research Assistants 🛠️ RAG TECH STACK 🐍 Python 🧠 Embedding Models 🗄️ Qdrant / Pinecone / Weaviate / FAISS 🔗 LangChain / LlamaIndex 🤖 LLMs 🧠 EASY WAY TO REMEMBER RAG = RETRIEVE + AUGMENT + GENERATE 🔎 Retrieve relevant knowledge ➕ 📚 Add it as context ➕ 🤖 Generate the answer 💡 RAG doesn’t magically make an LLM smarter. It gives the model access to relevant information at the moment it needs it. That’s what makes RAG one of the most important patterns for building practical GenAI applications. 🚀 #RAG #RetrievalAugmentedGeneration #GenerativeAI #creatorsearchinsights #datascience

Large Language Models are powerful, but they can struggle when they need information that isn’t in their training data or when answers require access to private, current, or specialized knowledge. That’s where RAG comes in. 🔍 WHAT IS RAG? RAG combines two main components: 🔎 Retriever → Finds relevant information 🤖 Generator → Uses that information to generate an answer Instead of asking an LLM to answer from memory alone, RAG gives it relevant context first. 🔄 HOW RAG WORKS 📄 Documents ⬇️ ✂️ Chunking Break documents into smaller pieces. ⬇️ 🧠 Embeddings Convert text into numerical representations. ⬇️ 🗄️ Vector Database Store the embeddings for efficient retrieval. ⬇️ ❓ User Query The user asks a question. ⬇️ 🔎 Retriever Find the most relevant chunks. ⬇️ 📚 Context Relevant information is added to the prompt. ⬇️ 🤖 LLM / Generator Generates a response using the retrieved context. ⬇️ 💬 Final Answer 🧩 THE TWO CORE PARTS 1️⃣ RETRIEVER 🔎 Its job is to find the most relevant information. Common techniques: 🔹 Vector Search 🔹 Keyword Search 🔹 Hybrid Search 🔹 Reranking 2️⃣ GENERATOR 🤖 Usually an LLM that takes: User Question + Retrieved Context and produces the final response. 🚀 WHY USE RAG? ✅ Use private documents ✅ Access updated information ✅ Ground answers in source material ✅ Reduce unsupported answers ✅ Build domain-specific AI assistants 🌍 REAL-WORLD APPLICATIONS 📚 Chat with PDFs 🏢 Enterprise Knowledge Bases 🎓 Educational Assistants ⚖️ Legal Document Search 🏥 Healthcare Information Systems 💬 Customer Support 📝 Research Assistants 🛠️ RAG TECH STACK 🐍 Python 🧠 Embedding Models 🗄️ Qdrant / Pinecone / Weaviate / FAISS 🔗 LangChain / LlamaIndex 🤖 LLMs 🧠 EASY WAY TO REMEMBER RAG = RETRIEVE + AUGMENT + GENERATE 🔎 Retrieve relevant knowledge ➕ 📚 Add it as context ➕ 🤖 Generate the answer 💡 RAG doesn’t magically make an LLM smarter. It gives the model access to relevant information at the moment it needs it. That’s what makes RAG one of the most important patterns for building practical GenAI applications. 🚀 #RAG #RetrievalAugmentedGeneration #GenerativeAI #creatorsearchinsights #datascience

🚨 Hace más de 30 años, en El Porvenir, Trujillo, nació la dinastía criminal de Los Pulpos. Lo que comenzó con la extorsión se expandió hacia la minería ilegal y, en su tercera generación, los secuestros se convirtieron en una de las principales actividades atribuidas al clan.

🚨 Hace más de 30 años, en El Porvenir, Trujillo, nació la dinastía criminal de Los Pulpos. Lo que comenzó con la extorsión se expandió hacia la minería ilegal y, en su tercera generación, los secuestros se convirtieron en una de las principales actividades atribuidas al clan.

@A_B_k U_M_R

cat clapping meme #cat #fyppppppppppppppppppppppp

cat clapping meme #cat #fyppppppppppppppppppppppp

yup all my chess talent went down hill from there

yup all my chess talent went down hill from there

#creatorsearchinsights #newtrendingproducts

#creatorsearchinsights #newtrendingproducts

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