Overview

Learn about Large Language Models (LLM) and how RAGs combine generative and retrieval-based AI models to extend the already powerful capabilities of LLMs. Get the knowledge you need about how a RAG works and how it’s assembled from component parts.

With our hands-on labs, you will develop a RAG using existing LLM and AI tools and apply RAG techniques to designs to solve problems.

Skills Covered

  • Harness the Power of LLMs
  • Gain Competitive Skills with RAGs
  • Advance Your Career

Prerequisites

  • Participants should have basic programming skills (preferably in Python), a foundational understanding of mathematics and statistics, some experience with data analysis.

Target Audience

  • AI/ML Developers
  • Software & Data Engineers
  • IT and QA Staff
  • Technical Managers and more

Course Curriculum

Module 1: LLM Overview

  •  Lab: Building an LLM Endpoint

Module 2: RAG Overview

  • Lab: Data Collection and Chunking

Module 3: Vector Databases and Embedding

  • Lab: Creating Embeddings and Ingesting Data

Module 4: RAG Techniques and Enhancements

  • Labs: Building the Final RAG Solution

Dates & Locations

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