Developing RAG Applications

2 Days

Live or E-learning

Delivery Method

Skill Level

$1,200 USD

Fee

Overview

This course offers a comprehensive understanding of key Retriever-Augmented Generation (RAG) concepts, techniques, and application strategies for building LLM-powered applications. It’s designed for both LLM Engineers and AI Developers looking to advance their skills in developing RAG-based LLM solutions. Participants will gain hands-on experience in building RAG applications, working with vector databases, document ingestion and indexing, document ranking, and generating outputs using LangChain.

Completing this course will help you:

Gain a deep understanding of RAG concepts and applications.
Configure and manage vector databases such as Chroma and Milvus.
Develop RAG-powered applications using Python, LangChain, and FastAPI or Streamlit.
Understand advanced RAG techniques to improve performance.

Curriculum

- Understand the fundamentals of RAG.
- Explore how RAG can help address the shortcomings of LLMs such as hallucination.
- Learn the key benefits of integrating RAG techniques in applications.
- Discover the role of vector databases in document retrieval systems.
- Learn to set up and configure vector databases like Chroma and Milvus.
- Explore indexing, storage, and retrieval mechanisms for efficient data handling.
- Learn the core components of LangChain and LlamaIndex.
- Build a document question-answering application from scratch.
- Implement indexing, retrieval, re-ranking, and generation techniques using LLMs.
- Apply various metrics to evaluate the performance of RAG.
- Work with common RAG evaluation frameworks.
- Use the LLM-as-a-judge technique to evaluate RAG.
- Design and build a functional Text-to-SQL RAG application.
- Apply the end-to-end process of RAG, from document ingestion to response generation.
- Test, evaluate and optimize your application for real-world scenarios.

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