Week 5 - Part 1 :(RAG, Vector Stores & Frameworks): RAG systems and Lang Chain and Semantic Kernel.

(RAG, Vector Stores & Frameworks): Go deeper by building Retrieval-Augmented Generation (RAG) systems and leveraging cutting-edge frameworks like LangChain and Semantic Kernel.

Retrieval-Augmented Generation, or RAG, is a revolutionary architectural pattern in AI that blends information retrieval systems with generative Large Language Models. This combination addresses the limitations of static LLMs, such as knowledge cutoffs and hallucinations, by enabling access to dynamic or private enterprise data without expensive retraining. In this video, we’ll explore the fundamentals of RAG, its components, and why it’s transforming enterprise AI. Let’s start by understanding the core problem RAG solves and how it empowers organizations to leverage up-to-date information for smarter, more reliable responses.

00:28
Static LLMs are powerful, but they’re limited by their training data. They can’t access new or proprietary information unless retrained, which is costly and slow. RAG solves this by integrating a retrieval system that fetches relevant data in real time, allowing the model to generate responses grounded in current, enterprise-specific knowledge. This approach mitigates hallucinations and ensures the model’s answers are accurate and contextually relevant. By overcoming knowledge cutoffs, RAG makes AI more adaptable and useful for business applications.

00:53
A standard RAG pipeline consists of three main stages: ingestion and processing, retrieval, and generation. First, documents are ingested, processed, and converted into vector embeddings, which are stored in a vector database. When a user submits a query, it’s embedded using the same model, and the database performs a similarity search to fetch the most relevant text chunks. Finally, these chunks are appended to the user’s query and fed to an LLM, which generates a precise response. This pipeline ensures that answers are always based on the most relevant and up-to-date information.

01:18
The first stage of a RAG pipeline is ingestion and processing. Here, text is extracted from documents and divided into manageable chunks. These chunks are then converted into vector embeddings using an embedding model and stored in a vector database. This process ensures that the data is ready for efficient retrieval and generation. By breaking down documents and embedding them, RAG systems can quickly access relevant information when needed, laying the foundation for accurate AI responses.

01:42
Once the data is processed and stored, the retrieval stage begins. When a user submits a query, it’s embedded using the same model as the stored chunks. The vector database then performs a similarity search to find the most relevant chunks of text. This ensures that the AI model receives contextually appropriate information, making its responses more accurate and tailored to the user’s needs. Retrieval is the heart of RAG, connecting queries with the right data.

02:01
The final stage in the RAG pipeline is generation. Here, the retrieved chunks are appended alongside the original user query into a template prompt. This prompt is then fed to a Large Language Model, which generates a response based on both the query and the provided context. This method ensures that the AI’s answers are grounded in real, relevant data, reducing hallucinations and improving reliability. Generation completes the RAG cycle, delivering precise and trustworthy information.

02:21
Chunking is a critical process in RAG, involving the breakdown of long documents into smaller, discrete segments of text. This is necessary because LLMs and embedding models have strict context window and token limitations. By creating focused chunks, RAG systems prevent embedding vectors from losing specific semantic meanings, ensuring that each piece of information retains its value. Chunking makes it possible to efficiently process and retrieve relevant data for AI generation.

Видео Week 5 - Part 1 :(RAG, Vector Stores & Frameworks): RAG systems and Lang Chain and Semantic Kernel. канала DynamicInterviewVerse