Retrieval-Augmented Generation (RAG) Deep-dive into building & RAG pipelines and enhancing performance

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Overview

Retrieval Augmented Generation (RAG) represents a breakthrough approach in AI development, combining the power of large language models with external knowledge retrieval systems. This comprehensive training program explores the cutting-edge technologies that are revolutionizing how AI systems access, process, and generate information based on dynamic data sources. Participants will gain hands-on expertise in designing and implementing sophisticated RAG architectures that are transforming industries from customer service to technical documentation and enterprise knowledge management.

The course offers an immersive journey through advanced RAG frameworks, focusing on vector databases, semantic search technologies, and prompt engineering techniques. By combining theoretical foundations with practical implementation, participants will learn to develop state-of-the-art RAG systems, understand complex retrieval mechanisms, and explore the frontiers of context-aware AI applications. The curriculum is designed to bridge the gap between academic research and real-world application, empowering developers and data scientists to leverage these powerful technologies in production environments.

Cognixia’s “Retrieval Augmented Generation” program will help participants gain proficiency in implementing advanced RAG architectures and develop a nuanced understanding of how these technologies can be applied to solve complex problems in information retrieval, knowledge management, and content generation. The course goes beyond traditional technical training by introducing cutting-edge concepts in vector embeddings, contextual relevance optimization, and the ethical considerations surrounding AI-assisted information systems.

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What you'll learn

  • Understanding the components of RAG
  • Building a RAG pipeline and implementing RAG systems
  • Using RAG for real-time applications
  • Deploying RAG systems and building production-ready applications
  • Building and working with advanced RAG architectures

Prerequisites

  • Basic understanding of Machine Learning and Natural Language Processing (NLP)
  • Familiarity with Large Language Models (LLMs) (e.g., OpenAI GPT, Google Gemini, DeepSeek)

Curriculum

  • What is RAG
  • Differences between standard LLMs and RAG
  • Why RAG? Benefits and use cases
  • Retrieval: Document stores, vector databases, embeddings
  • Augmentation: How retrieved data enhances LLM responses
  • Generation: Integrating retrieved knowledge with an LLM
  • Selecting an LLM (OpenAI, DeepSeek, Mistral, Llama)
  • Choosing and indexing a knowledge base (Vector DBs: Pinecone, Weaviate, ChromaDB)
  • Implementing embedding models (sentence-transformers, OpenAI embeddings)
  • Querying and retrieving relevant documents
  • Setting up a RAG system with LangChain
  • Using FAISS or ChromaDB for retrieval
  • Querying an external knowledge base
  • Fine-tuning RAG retrieval parameters
  • Optimizing retrieval with better embeddings
  • Hybrid search: Combining keyword-based and vector retrieval
  • Filtering and reranking retrieved documents
  • Multi-modal RAG (text, images, structured data)
  • RAG with knowledge graphs
  • Using RAG for real-time applications
  • Deploying RAG models with APIs (FastAPI, Flask)
  • Scaling RAG with cloud services (AWS, Azure, GCP)
  • Monitoring and evaluating RAG performance
  • Integrating RAG into a chatbot or search assistant
  • Optimizing inference speed and accuracy
  • Deploying RAG on a cloud platform

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FAQs

Retrieval Augmented Generation (RAG) is an innovative AI architecture that enhances large language models by connecting them with external knowledge sources. This approach allows AI systems to access up-to-date information, reduce hallucinations, and provide more accurate, contextually relevant responses by retrieving and incorporating information from databases, documents, and other sources.
While standard LLMs rely solely on knowledge encoded in their parameters during training, RAG systems actively retrieve relevant external information at inference time. This allows RAG systems to access fresher data, cite sources, handle domain-specific knowledge, and provide more accurate responses without requiring constant model retraining.
This Generative AI course is ideal for AI engineers, data scientists, NLP specialists, software developers, knowledge management professionals, and technical leaders looking to implement advanced AI systems that can leverage external knowledge sources for improved accuracy and capabilities.
For this course, participants will need to have a basic understanding of machine learning and natural language processing, as well as familiarity with large language models.