Mastering Retrieval Augmented Generation (RAG) IN LLMs

[AD-TEAM]

[NEW]Mastering Retrieval Augmented Generation (RAG) IN LLMs
Published 8/2024
Duration: 2h20m | .MP4 1280x720, 30 fps® | AAC, 44100 Hz, 2ch | 995 MB
Genre: eLearning | Language: English

Quick walkthrough of RAGs

What you'll learn
Retrieval Augmented Generation (RAG) IN LLMs
RAG using PDF
RAG Using CSV file
Laoding LLM Models
Ollama
Langchain

Requirements
Python
Generative AI basics
Interest in GEN-AI

Description
In today's rapidly evolving landscape of artificial intelligence, Large Language Models (LLMs) have emerged as powerful tools for a wide range of applications. However, to truly unlock their full potential, we need to equip them with the ability to access and process external information. That's where Retrieval Augmented Generation (RAG) comes into play.
This course will provide you with a comprehensive understanding of RAG and its applications in enhancing LLM capabilities. You'll learn how to effectively retrieve relevant information from external sources and integrate it into the LLM's responses, making them more informative and accurate.
Course Objectives
Gain a solid understanding of generative AI and LLMs.
Explore the concept of RAG and its benefits.
Learn how to use Langchain to import and interact with LLMs.
Master the process of extracting context from PDFs and CSVs using Ollama.
Apply RAG techniques to enhance LLM performance in various tasks.
Course Structure
Introduction to Gen-AI using LLMs:
This introductory lecture will provide a foundational understanding of generative AI and LLMs.
Introduction to RAG:
Explore the concept of RAG, its benefits, and how it works.
Using Langchain to Import LLMs:
Learn how to effectively import and interact with LLMs using the Langchain library.
Using Ollama to Extract Context from PDFs for LLM:
Discover how to extract relevant information from PDFs and incorporate it into LLM responses.
Using Ollama to Extract Context from CSVs for LLM:
Learn to extract context from CSV files and integrate it into LLM responses.
Why Choose This Course?
Practical Focus:
Gain hands-on experience with RAG techniques and tools.
Expert Guidance:
Learn from experienced instructors in the field of generative AI.
Comprehensive Coverage:
Explore the entire RAG workflow from importing LLMs to extracting context.
Real-World Applications:
Discover how RAG can be applied to various tasks and industries.
Enroll today and unlock the power of RAG to enhance your LLM applications!
Who this course is for:
BEGINNER AI Developers
data Scientists
Analyst
Python Developer

More Info

[To see links please register or login]

[To see links please register or login]

[To see links please register or login]