What is RAG and How it Works? #genai #llm #generativeai #coding
Summary
RAG solves AI hallucination by chunking large private documents into searchable segments and retrieving relevant context for each user query. The system then augments the user's question with the retrieved context before sending it to the AI, enabling factually accurate answers without guessing.
Summary generated by brickster.ai from the video transcript.
More from Ease With Data
TutorialsDatabricks All Purpose vs Serverless Computes #databricks #databrickstutorial
Databricks all-purpose compute provides traditional persistent clusters with manual configuration and continuous uptime costs. Serverless compute offers fully managed infrastructure that boots in seconds, scales dynamically, and bills only for active execution time.
TutorialsLangChain Tutorial - Build an AI Agent with Tools | Chapter 10
This video teaches how to build AI agents using the LangChain framework in Python. It demonstrates initializing a model, registering Python functions as structured tools, and creating both single agents and supervisor-sub-agent architectures.
NewsSpark vs SDP, What the difference? #spark #pyspark #dlt
Traditional Apache Spark requires developers to manually write complex procedural code for processing steps, state management, and checkpoints. Spark declarative pipelines allow users to define the desired final data state in SQL or Python while the engine automatically handles execution order, dependencies, and incremental processing.
TutorialsBuild Your First AI Agent + LLM Tool Calling Explained | Chapter 09
Agents operate through tool-calling loops where the LLM requests specific tool execution with arguments, code executes the tools, and observations return for the LLM to process. The video demonstrates building a Python agent using Grok API with two tools that execute sequentially: retrieving weather for a city and suggesting an activity based on temperature.
NewsHow Distillation Works? #claude #kimik3
Knowledge distillation trains a smaller student AI model by using prompts and reasoning outputs from a larger, more advanced teacher model. This process allows the cheaper student model to replicate the expensive teacher's advanced capabilities at a fraction of the computational cost and without accessing its source code.
NewsRAG Explained + Build a RAG App From Scratch in Python using LLM | Chapter 08
This video teaches the core concepts of retrieval-augmented generation and demonstrates how to build a complete RAG application from scratch in Python using a Groq language model. The tutorial covers a seven-step pipeline including document ingestion, token-based text chunking, vector embedding generation, in-memory storage, similarity search, prompt augmentation, and response generation.