Ementorhub

What you'll learn

Demonstrate Large Language Model capabilities in Natural Language based Automations.

Demonstrate the use of RAG Applications in a range of problems they can solve.

Use Vector Databases as a Storage Medium of Language Embeddings in RAG Applications.

Develop RAG Applications using LLM Frameworks, Models and Vector Databases.

Skills you'll gain

Artificial Intelligence and Machine Learning (AI/ML)

Data Store

Application Development

Large Language Modeling

Data Storage Technologies

OpenAI

Prompt Engineering

Generative AI

Natural Language Processing

ChatGPT

There is 1 module in this course

The capabilities of LLMs are not to be kept confined within the tools like ChaGPT or Google Gemini or Anthropic Claude. You can leverage the powerful Natural Language Capabilities of LLMs applied on your organizational data to create amazing automations and applications that are called Retrieval Augmented Generation or RAG Applications. Some of the key components of the course are learning prompt Engineering for RAG Applications, working with Agents, Tools, Documents, Loaders, Splitters, Output Parsers and so on, which are essential ingredients of RAG Applications. Participants should have a basic understanding of Python programming and a foundational knowledge of Large Language Models (LLMs) to make the most of this course. By the end of this course, you'll be able to develop RAG applications using Large Language Models, LangChain, and Vector Databases. You will learn to write effective prompts, understand models and tokens, and apply vector databases to automate workflows. You'll also grasp key LangChain concepts to build simple to medium complexity RAG applications.