Jan 16
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Welcome back to Summarized AI! 👋 In this video, we break down one of the most confusing topics in AI development: Why do we still need RAG if we already have fine-tuning? This video is packed with valuable insights for AI beginners and professionals. You’ll learn: 🔍 What You Will Learn 1️⃣ Understanding Pretrained AI Models How LLMs are trained on massive datasets like websites, books, code, and more Why models like GPT-4 are considered generalized How specialized models like CodeLlama or Codex learn from code repositories 2️⃣ What Fine-Tuning Really Does How fine-tuning adapts an AI model to your tone, writing style, and domain Why fine-tuning doesn’t update your model with new or real-time information Why the model’s knowledge becomes frozen after training 3️⃣ Why Fine-Tuning Cannot Replace RAG Fine-tuned models cannot answer real-time questions (e.g., today’s weather) Retraining is expensive and slow No way to keep the model constantly updated 4️⃣ How RAG Solves These Problems RAG retrieves fresh information directly from your database/documents Embeddings + semantic search → accurate real-time answers Your AI stays current without retraining the model 🧠 Vector Embeddings Demystified We break down embeddings with simple examples: How documents are chunked How each chunk is converted into a vector of 1536 dimensions using text-embedding-3-small Static vs contextual embeddings Why the word “Apple” can mean a fruit or a tech company How vector databases (like Postgres pgvector) store and search these embeddings Why vector size never changes, even for long sentences #RAG #RetrievalAugmentedGeneration #FineTuning #FineTuningVsRAG #VectorEmbeddings #SemanticSearch #LLM #LLMTutorial #OpenAI #OpenAIEmbeddings #TextEmbedding3Small #VectorDatabase #Pgvector #Postgres #PostgresVector #AISearch #MachineLearning #AIExplained #EmbeddingModels #ContextualEmbeddings #StaticEmbeddings #LLMFineTuning #AIKnowledgeBase #Chunking #DocumentAI #GenerativeAI #AIDevelopment #GPT4 #OpenAITutorial #AIVectors #RetrievalLLM #RAGArchitecture #RAGWorkflow #EmbeddingDimensions #1536Embeddings #HowEmbeddingsWork #SummarizedAI #AIForDevelopers #AIEducation #AITraining #LLMSearch #ChatGPTRAG