Retrieval Augmented Generation (RAG) Introduction (RXM403)

Course code: RXM403

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.

Key Benefits for You:

  • Live, instructor-led hands-on labs
  • Develop unique skills
  • Use RAG to refine LLM outputs

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Course dates

Starting date: Upon request

Type: In-person/Virtual

Course duration: 1 day

Language: en/cz

Price without VAT: 660 EUR

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Course description

The Linux Foundation has partnered with the AI/ML experts at rx-m to offer this course to the community.

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

How will this course benefit me?

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

Target group

AI/ML Developers. Software & Data Engineers, IT and QA Staff. Technical Managers and more!

Course structure

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

Prerequisites

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

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