Unraveling the Complexities of RAG: Enhancing Data Retrieval Beyond Traditional Methods

In the intricate world of machine learning, large language models and natural language processing; the concept of Retrieval Augmented Generation (RAG) stands out as a beacon of innovation.

The realm of RAG is hot trend today when talking about building AI applications. So, you should be spending time learning about it.

This article aims to explore the untapped potential of RAG, showcasing how it transcends conventional boundaries, offering more efficient, accurate, and contextually rich data retrieval methods.

Understanding the Core of RAG

Breaking Down Traditional RAG

In a standard RAG setup, a document is first split into chunks.

Then, those chunks are converted into an embedding vector.

Finally those embedding vectors are indexed in a vector database.

When querying using RAG techniques, the original query is turned into an embedding vector, and similar indexed vectors are retrieved from the database.

Those retrieved indexed vectors are used as a context to build the prompt executed by LLMs.

However, this method, though effective, has limitations, especially when dealing with complex or large documents.

Strategies to improve your RAG pipeline

The Parent-Child Strategy

Imagine a large document as a family tree.

Just like a family tree branches out to various members, a document can be broken down into smaller, more manageable chunks.

Those chunks, with their unique vector representations or embedding, are indexed, but with a reference to the 'parent' document.

The idea is that 'parent' document that holds the broader context.

Instead of indexed chunks have more chance to contain a unique concept, so they are great for indexing the data for similarity search.

When querying, we get the similar indexed vectors. Then we get the common parent documents.

Then, those common parent documents, instead of retrieved indexed vectors, are used as a context to build the prompt executed by LLMs.

Expanding Horizons with Hypothetical Questions

RAG can be further innovated by indexing documents based on hypothetical questions they could answer.

Imagine an LLM generating potential questions for a document during the indexing phase.

These questions along with the chunks, once vectorized, become new indices.

When a real query aligns with these hypothetical questions, the original document is retrieved, ensuring the response is grounded in comprehensive context.

Leveraging Summaries for Enhanced Retrieval

Another strategy involves indexing documents based on their summaries.

Summarizing complex documents, especially those containing data like tables, and indexing these summaries can significantly enhance the accuracy of data retrieval.

We need to store a reference to the original document when indexing. So, we can use it as part of the context of the prompt.

This approach is particularly useful when dealing with non-textual data, ensuring the queries align more closely with the semantic essence of the document.

Implementing Best Practices in RAG

Contextualized Prompting

For optimal results, use prompts that provide detailed context and instructions.

Assigning a persona to the model can tailor the responses more accurately to the desired expertise.

For example, "You are a senior business analyst who is an expert in strategic planning and creating mission, vision, and core value statements for organizations".

Information Retrieval Strategy

Ensure that the model uses only provided documents for context.

This approach maintains the relevance and accuracy of the information retrieved.

Experimentation with Few-Shot Examples

Utilize example selectors to provide a framework for expected prompts and responses.

Tools like Similarity, MMR, or NGram Overlap selectors are crucial in refining the selection of examples for better alignment with the prompt.

Utilizing Tools and Plugins

Incorporate additional tools or plugins, like calculators or code executors, to enhance the functionality of your RAG setup.

This multi-tool approach can significantly streamline the data retrieval process.

Optimizing the Pipeline

Maintain an efficient pipeline by chunking texts into sections of 150-200 tokens with an overlap of 0-30 tokens.

This segmentation aligns with the average English paragraph, improving the vector-based similarity search.

Choice of Embedding Tools

Found that sentence-transformers work just fine for embedding your documents. You can use free and OpenAI options.

Conclusion

In conclusion, the realm of RAG is evolving, breaking the shackles of traditional data retrieval methods.

Today, building a RAG pipeline is a synonym of AI solution.

By embracing innovative approaches like the parent-child relationship, indexing through hypothetical questions, and leveraging summaries, RAG can offer more precise, context-rich, and efficient data retrieval.

The best practices outlined here serve as a roadmap for anyone looking to harness the full potential of RAG, paving the way for a more intelligent and intuitive future in data processing and retrieval.

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