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Learn advanced RAG techniques in this advanced RAG course. Explore pre- and post-retrieval optimization with LangChain, and build intelligent, scalable applications with hands-on projects.

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RAG-Final.tar.gz

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MultiQuery

RAG-Fusion

Decomposition

SemanticRouting

RoutingClassifier

CrossEncoderReranking

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HyDE

Master advanced retrieval-augmented Generation (RAG) techniques in this comprehensive course for experienced learners. You’ll gain hands-on experience implementing advanced RAG systems with LangChain, equipping you to create intelligent, efficient AI applications.

The course begins by revisiting the core principles of RAG and then dives into pre-retrieval optimization techniques, focusing on strategies like indexing and query formulation. Next, you’ll explore post-retrieval optimization methods, such as RAG-Fusion and cross encoder reranking, to refine and enhance retrieved data.

Finally, you’ll work on a practical project to build a fully functional RAG application, applying advanced pre- and post-retrieval optimization techniques. After completing this course, you’ll have the expertise to design intelligent, scalable systems that harness the full potential of RAG.

Advanced RAG Techniques: Choosing the Right Approach

To create $n$ variations of the user’s question

In the step of query generation for RAG-Fusion, what does the template prompt instruct the LLM to do?

In the step of query generation for RAG-Fusion, what does the template prompt instruct the LLM to do?

To convert text into numerical embeddings

To split large documents into smaller, manageable chunks

What is the purpose of using the `RecursiveCharacterTextSplitter` for implementing RAG?

What is the purpose of using the <code>RecursiveCharacterTextSplitter</code> for implementing RAG?

Which library is used to create a vector store for document chunks?

Which library is used to create a vector store for document chunks?

To assign scores based on the relevance of documents to multiple queries

Within RAG-Fusion, how does reciprocal rank fusion (RRF) function?

Within RAG-Fusion, how does reciprocal rank fusion (RRF) function?

To improve the relevance of search results

To reduce the computational cost of search engines

To enhance the visual layout of search results

What is the primary purpose of reranking in search systems?

What is the primary purpose of reranking in search systems?

It reduces the number of documents to be retrieved.

It enhances the semantic understanding of the user’s intent.

It increases the speed of the retrieval process.

What is a primary advantage of generating multiple query variations in RAG-Fusion?

What is a primary advantage of generating multiple query variations in RAG-Fusion?

To create a structured prompt for the language model

To generate embeddings for document chunks

What is the role of the `ChatPromptTemplate` in RAG implementation?

What is the role of the <code>ChatPromptTemplate</code> in RAG implementation?

Applying the reciprocal rank fusion formula

In the RAG-Fusion technique, what ensures the generation of deterministic outputs during query generation?

In the RAG-Fusion technique, what ensures the generation of deterministic outputs during query generation?

To split the retrieved documents into smaller chunks

To combine multiple retrieval results using rank fusion

To evaluate the semantic similarity between the query and each retrieved item

To generate multiple variations of the user’s original query

In Cross Encoder reranking, what is the primary role of the Cross Encoder model?

In Cross Encoder reranking, what is the primary role of the Cross Encoder model?

It assigns new relevance scores to retrieved items based on deep semantic analysis.

It retrieves documents based on keyword matching.

It uses a generative model to rephrase the user’s query.

It splits large documents into smaller chunks for retrieval.

How does Cross Encoder reranking differ from traditional ranking methods?

How does Cross Encoder reranking differ from traditional ranking methods?

It generates multiple candidate responses based on the user query and retrieved documents.

It refines the ranking of retrieved documents to prioritize those most relevant to the user’s query.

It encodes the user query and retrieved documents for efficient comparison during retrieval.

It filters out irrelevant documents retrieved from the corpus based on pre-defined criteria.

In the context of Retrieval-Augmented Generation (RAG) pipelines, what is the primary function of a Cross Encoder Reranker?

In the context of Retrieval-Augmented Generation (RAG) pipelines, what is the primary function of a Cross Encoder Reranker?

Test your understanding of post-retrieval techniques for advanced RAG.

Getting Started

Introduction to Retrieval-Augmented Generation (RAG)

Advanced RAG: Pre-Retrieval (Optimizing Indexing)

Advanced RAG: Pre-Retrieval (Optimizing Query)

Build a RAG Using LangChain with Google Gemini

Advanced RAG: Post-Retrieval Process

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

Conclusion

Quiz: Post-Retrieval Techniques for Advanced RAG