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  • Writer's pictureShawna Applequist

From RAGs to Riches: How Retrieval Augmented Generation is Improving LLMs

Large Language Models (LLMs) are at the forefront of artificial intelligence (AI) research and application. LLMs are invaluable across a wide range of fields due to their ability to understand, interpret, and generate human language in a way that is both meaningful and contextually relevant. As the desire to implement these types of systems in numerous sectors grows, so does the demand for LLMs to provide accurate, reliable, and relevant information. The quality of information provided by LLMs can have significant real-world consequences. The expectation is not only for LLMs to understand and generate text but also to provide insights that are factually correct, up-to-date, and directly applicable to the user's needs.

To meet these demands, LLMs must overcome several challenges, many of which stem from their training processes. Addressing these issues is critical to enhance the utility and reliability of LLMs and ensure that they can effectively meet user expectation in real-world applications. Three primary issues can hamper their efficacy:

Hallucinations: This term refers to instances where LLMs generate plausible but factually incorrect or nonsensical information. These errors can range from minor inaccuracies to complete fabrication. This issue poses a particular risk in fields where misinformation can have serious consequences.

Inaccurate Data: The adage "garbage in, garbage out" is particularly relevant to LLMs. The data used to train models could contain errors, biases, or outdated information. Since LLMs learn to predict responses based on their training data, inaccuracies in this data lead to inaccuracies in the model's outputs.

Irrelevant Information: LLMs sometimes may provide information that, while factually correct, is not relevant to the user's query. This is due to the model's inability to understand context or the nuances of a question fully. Irrelevance reduces the practical utility of the model and can lead to user frustration.

What is to be done about these challenges? They highlight the need for innovative approaches to improve the training and function of LLMs, which is where Retrieval Augmented Generation (RAG) comes into play.

RAG is designed to enhance the capabilities of LLMs by addressing these limitations. At its core, RAG involves the integration of a knowledge base that lives outside of the LLM's training corpus. Instead of relying solely on pre-learned patterns from training data, RAG-equipped LLMs can pull in up-to-date information from external sources, directly reducing the risk of hallucinations, inaccuracies, and irrelevant responses.

The RAG process involves several key steps:

Input Query: As you would expect, the process begins with a user's query, which can be anything from text to code to audio. The quality and specificity of the input query can significantly influence the effectiveness of the next steps and the final result.

Retrieval: Next, the system per a relevancy search through a predefined data source or knowledge base. This retrieval can either be sparse or dense. With sparse retrieval, keyword matching, and taxonomic classification are used to find relevant information. Like using a traditional index in a library to find books on a specific topic. Dense retrieval uses semantic search techniques to find information that is contextually related to the input query, even if it doesn't share exact keywords. This is similar to asking a knowledgeable librarian who understands your query's context and can find books that answer your question, even if they're not explicitly about the stated topic. Often, these methods are used in tandem to ensure a comprehensive search.

Augmentation: In this phase, the retrieved information is integrated with the initial query. The LLM uses this augmented dataset to generate a response which is not only accurate but also rich in context and nuance.

While there are many pieces that contribute to an effective RAG implementation, here at WAND, we are particularly poised to help your company with the sparse retrieval step of this process. Taxonomies are hierarchical classifications that organize knowledge, enabling structured information retrieval. In the context of RAG, these metadata models serve as a roadmap, guiding the retrieval process through the vast and complex landscape of data.

The top five uses in this context are:

Structured Query Matching: Taxonomies helps in structuring queries to match against categorized data more effectively. By understanding the category or context to which a query belongs, RAG systems can more accurately pinpoint the information that is most relevant.

Improving Precision and Relevance: The hierarchical nature of the taxonomy model allows for narrowing down search results quickly to the most specific and relevant information. This precision reduces the data noise and increases the overall relevance of the augmentation process later.

Disambiguation: By categorizing information, taxonomies help in disambiguating terms and concepts. This ensures that the retrieval process fetches information relevant to the specific context of the query, rather than unrelated data that happens to share the same keywords.

Enhancing the Quality of Responses: With access to more relevant and precise information, the LLM can generate responses that are not only accurate but also rich in content. This can significantly improve the UX by providing responses that are informed, authoritative, and contextually appropriate.

Feedback Loops: Incorporating taxonomies into RAG facilitates the creation of feedback loops where the system can learn from its interactions. By categorizing queries and responses, RAG systems can better understand the relationships between different types of information and refine their retrieval strategies over time.

As we continue to travel deeper into the era of AI, the integration of RAG processes into LLMs represents a promising path toward more intelligent, reliable, relevant information systems. By combining the predictive power of LLMs with the precision of RAG, companies can develop AI systems that can better understand, interact, and impact the world around them.


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