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Advanced RAG Techniques: Choosing the Right Approach

Gain insights into RAG systems by exploring pre- and post-retrieval optimization techniques, and discover how to build intelligent applications using LangChain with practical hands-on experience.

Intermediate

28 Lessons

Certificate of Completion

Gain insights into RAG systems by exploring pre- and post-retrieval optimization techniques, and discover how to build intelligent applications using LangChain with practical hands-on experience.

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Explanations

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Explanations

This course includes

1 Project

23 Playgrounds

Course Overview

What You'll Learn

Course Content

Recommendations

Course Overview

The course explains RAG, which combines information retrieval and generative models. Learners will gain hands-on experience with state-of-the-art technologies throughout the course by implementing RAG systems using LangChain. The course begins with an introduction to the core principles of RAG. As the course progresses, you’ll explore pre-retrieval optimization techniques, including strategies for optimizing indexing and query formulation, as these techniques are covered in depth. You will then focus on po...Show More

The course explains RAG, which combines information retrieval and generative models. Learners will gain hands-on experience with...Show More

TAKEAWAY SKILLS

Generative AI

Natural Language Processing

What You'll Learn

An understanding of the core principles of retrieval-augmented generation (RAG) and how it improves the accuracy of large language models

Familiarity with different RAG implementation approaches and how to choose the most suitable approach for specific applications

Hands-on experience implementing RAG models using LangChain

The ability to use advanced pre-retrieval optimization techniques for indexing and query optimization

Hands-on experience in post-retrieval processing methods, like RAG-fusion, to refine retrieved information for more accurate results

The ability to design and develop RAG-based chatbots

An understanding of the core principles of retrieval-augmented generation (RAG) and how it improves the accuracy of large language models

Course Content

Getting Started

2 Lessons

Get familiar with RAG to enhance factual accuracy and build robust NLP applications.

Introduction to the Course

Course Structure

Introduction to Retrieval-Augmented Generation (RAG)

6 Lessons

Look at the integration of retrieval-based and generative models for enhanced NLP tasks.

Choosing the Right Approach for Your Application

Quiz: Introduction to RAG

Advanced RAG: Pre-Retrieval (Optimizing Indexing)

7 Lessons

Break apart strategies to optimize indexing, chunking, and data cleaning for efficient retrieval.

What Is Pre-Retrieval Optimizing Indexing?

Chunking Strategies for Text Processing

Enhancing Data Granularity: Data Cleaning

Multi-Representation Indexing

Self-Querying Retrieval (SQR)

Parent Document Retrieval (PDR)

Quiz: Pre-Retrieval Techniques for Optimizing Indexing

Advanced RAG: Pre-Retrieval (Optimizing Query)

8 Lessons

Break down complex ideas in pre-retrieval query optimization for enhanced RAG accuracy.

What Is Pre-Retrieval Query Optimization?

Multi-Query

Decomposition

Step-Back Prompting

Hypothetical Document Embeddings (HyDE)

Semantic Routing

Routing with LLM-Based Classifier

Quiz: Pre-Retrieval Techniques for Optimizing Query

Advanced RAG: Post-Retrieval Process

4 Lessons

Enhance RAG response quality with post-retrieval optimization, RAG-fusion, and Cross Encoder reranking.

What Is Post-Retrieval Optimization?

Reranking: RAG-Fusion

Reranking: Cross Encoder Reranking

Quiz: Post-Retrieval Techniques for Advanced RAG

Talk to Your Web Page: A RAG-Powered Chat Interface

Project

Conclusion

1 Lessons

See how it works integrating RAG techniques for enhanced NLP applications and community engagement.

Wrap Up

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