Jan 16
Loading video player...
Large language models are powerful — but they only know what they were trained on. So how do you build AI that understands your documents, private data, or internal knowledge? That’s where RAG, embeddings, and vector databases come in. This video explains how Retrieval-Augmented Generation works, why it matters, and how these tools let you build AI systems that answer questions using your own data — without expensive fine-tuning. ━━━━━━━━━━━━━━━━━━ ❓ THE PROBLEM RAG SOLVES ━━━━━━━━━━━━━━━━━━ LLMs like ChatGPT have two big limits: • Knowledge stops at their training cutoff • They can’t access your private documents Uploading files isn’t enough — the model won’t remember them. Fine-tuning sounds tempting, but it’s costly, slow, and requires retraining every time data changes. RAG solves this by retrieving relevant information at query time and giving it to the model as context. No retraining. Instant updates. Clear sources. ━━━━━━━━━━━━━━━━━━ 🧠 WHAT ARE EMBEDDINGS? ━━━━━━━━━━━━━━━━━━ Embeddings turn text into vectors that represent meaning. Why this matters: • Similar meanings → similar vectors • Enables semantic search, not keyword search “Dog” is closer to “puppy” than to “economics” — mathematically. We explain how embeddings work, common providers (OpenAI, Cohere, open-source), and how to choose the right model. ━━━━━━━━━━━━━━━━━━ 📦 VECTOR DATABASES EXPLAINED ━━━━━━━━━━━━━━━━━━ Embeddings need fast search — that’s what vector databases do. Popular options: • Pinecone – managed & scalable • Chroma – open source & local • Weaviate – hybrid search • Qdrant – high performance • pgvector – vectors in PostgreSQL The right choice depends on scale, budget, and infrastructure. ━━━━━━━━━━━━━━━━━━ 🔄 HOW RAG WORKS (STEP BY STEP) ━━━━━━━━━━━━━━━━━━ A typical RAG pipeline: 1️⃣ Split documents into chunks 2️⃣ Generate embeddings 3️⃣ Store them in a vector database 4️⃣ Embed the user’s query 5️⃣ Retrieve the most relevant chunks 6️⃣ Inject them into the prompt 7️⃣ Generate an answer — often with citations Simple idea. Powerful results. ━━━━━━━━━━━━━━━━━━ ✂️ CHUNKING MATTERS ━━━━━━━━━━━━━━━━━━ Chunk size affects answer quality: • Too big → wasted context • Too small → lost meaning We cover: • Fixed chunks with overlap • Sentence-based splitting • Semantic chunking • Recursive strategies There’s no one-size-fits-all approach. ━━━━━━━━━━━━━━━━━━ 🏗️ BUILDING A RAG SYSTEM ━━━━━━━━━━━━━━━━━━ We walk through building a complete RAG setup: • Document ingestion • Embedding generation • Vector storage • Retrieval & filtering • Prompt design with sources Tools like LangChain and LlamaIndex help — but understanding the basics is key. ━━━━━━━━━━━━━━━━━━ ⚖️ RAG VS FINE-TUNING ━━━━━━━━━━━━━━━━━━ Use RAG when: • You need factual answers • Data changes often • You want citations • You want lower cost Use fine-tuning when: • You need behavioral changes • You want a specific writing style • Data is stable Many real systems use both. ━━━━━━━━━━━━━━━━━━ ⚠️ COMMON RAG CHALLENGES ━━━━━━━━━━━━━━━━━━ RAG isn’t magic: • Retrieval can miss context • Context windows are limited • Chunking can split meaning We cover solutions like: • Hybrid search • Reranking • Query expansion • Evaluation metrics ━━━━━━━━━━━━━━━━━━ 💬 SHARE YOUR RAG PROJECT ━━━━━━━━━━━━━━━━━━ What are you building with RAG? What problems are you hitting? 👇 Like 👍 & subscribe for more practical AI architecture explanations. #RAG #embeddings #vectordatabases #aiarchitecture #llm #semanticsearch #langchain #Chroma #pinecone #openai #machinelearning #AI2026