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The course explains advanced RAG concepts, showcasing their implementation through LangChain for enhanced practical learning and application.

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RAG-Final.tar.gz

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MultiQuery

RAG-Fusion

Decomposition

SemanticRouting

RoutingClassifier

CrossEncoderReranking

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HyDE

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 post-retrieval optimization, learning about reranking methods like RAG-Fusion and Cross Encoder reranking. 

Finally, you will work on a hands-on project to build an RAG application by applying the concepts learned in the course. By the end of this course, you’ll have gained sufficient experience and knowledge to build more intelligent systems using RAG.

Advanced RAG Techniques: Choosing the Right Approach

It reduces the need for any training data.

It simplifies the model architecture by using a single monolithic model.

It allows for dynamic and automatic adaptation to new query patterns without code changes

It guarantees the elimination of misclassified queries.

What is the primary advantage of using an LLM-based classifier for routing user queries in a large language model application?

What is the primary advantage of using an LLM-based classifier for routing user queries in a large language model application?

It reduces the number of documents retrieved from the corpus, making the process faster.

It enables the model to prioritize retrieving documents that address all aspects of the decomposed query.

It allows the model to find documents with specific keywords from each decomposed element.

It facilitates the retrieval of factual information directly from a knowledge base.

In RAG with query decomposition, a complex user query is broken down into a series of more focused sub-queries. How does this process enhance the retrieval of relevant documents compared to a traditional retrieval approach that analyzes the entire query at once?

In RAG with query decomposition, a complex user query is broken down into a series of more focused sub-queries. How does this process enhance the retrieval of relevant documents compared to a traditional retrieval approach that analyzes the entire query at once?

It encourages the model to reason about the underlying goal or intent behind the user’s question.

It allows the RAG system to directly access a specific knowledge base relevant to the user’s query.

It helps the model identify similar phrasings or synonyms in the corpus for improved retrieval.

It reduces the computational cost of encoding the user query for retrieval.

In the context of RAG, what is the primary benefit of including a step-back question before the original user query?

In the context of RAG, what is the primary benefit of including a step-back question before the original user query?

Converting the query into an image for better visualization.

Directly assigning the query to a pre-defined category (e.g., sports, finance).

Making sure all characters in the query are lowercase.

Transforming the query into a numerical vector that represents its semantic meaning.

When creating text embeddings for semantic routing in LLMs, what process best captures the meaning of a query?

When creating text embeddings for semantic routing in LLMs, what process best captures the meaning of a query?

Cosine similarity might not differentiate well between very similar categories.

Cosine similarity cannot handle multi-lingual text embeddings.

Cosine similarity does not consider the contextual meaning of words.

Cosine similarity is computationally expensive for small datasets.

What is a key challenge when using cosine similarity for routing queries based on text embeddings in LLM-based systems?

What is a key challenge when using cosine similarity for routing queries based on text embeddings in LLM-based systems?

It retrieves actual documents from a database that exactly match a given query.

It leverages large language models to generate summaries of existing documents.

It creates hypothetical document embeddings that capture the ideal response to a query.


It requires relevant labels to train the system for accurate retrieval.

Which statement is true about HyDE as described in the paper by Luyu Gao?

Which statement is true about HyDE as described in the paper by Luyu Gao?

To directly process and transform the user query.

To pass the user query through the pipeline without modification.

To log the user query for auditing purposes.

To format the user query into a specific language.

Why is the `RunnablePassthrough` component used in the LangChain pipeline for routing queries?

Why is the <code>RunnablePassthrough</code> component used in the LangChain pipeline for routing queries?

The computational cost of maintaining and running multiple LLMs.


The accuracy of the LLM used for classification can lead to sending queries to the wrong domain.

Users might not understand why their query gets routed to a specific LLM.

It’s impossible to define a comprehensive set of categories for all possible user queries.


Routing with LLM-based classifiers can be beneficial, but it also comes with potential drawbacks. What is a huge challenge associated with this approach?

Routing with LLM-based classifiers can be beneficial, but it also comes with potential drawbacks. What is a huge challenge associated with this approach?

Test your understanding of pre-retrieval techniques for optimizing query of advanced RAG.

Getting Started

Introduction to Retrieval-Augmented Generation (RAG)

Advanced RAG: Pre-Retrieval (Optimizing Indexing)

Advanced RAG: Pre-Retrieval (Optimizing Query)

Advanced RAG: Post-Retrieval Process

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

Conclusion

Quiz: Pre-Retrieval Techniques for Optimizing Query