RAG : How to improve the relevance of LLM on enterprise data

In this article, we examine how Retrieval-Augmented Generation (RAG) systems extend the capabilities of Large Language Models (LLMs), focusing on their reliability, efficiency, transparency and flexibility. We propose an in-depth analysis of their architecture and main components.

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Why take an interest in RAG Systems?

Modern LLMs are remarkable for their ability to provide tailored answers in seconds, where humans could spend hours. However, they can sometimes present biases or produce factually incorrect answers, often due to gaps in their training corpus. These gaps can be attributed to the novelty, scarcity or confidentiality of the data available.

For critical applications, such as interpreting legal clauses or validating sensitive information, the ability to generate accurate answers from verified sources becomes essential. This is where RAG systems come into their own, fitting in perfectly with "knowledge-intensive applications". These systems combine the power of LLMs with controlled knowledge bases, while guaranteeing the transparency of the sources used.

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Defining a RAG System

A RAG system is defined by its ability to enrich language models via a targeted search of a defined corpus. It is based on a modular architecture that exploits a set of advanced techniques to transform raw data into contextualized, accurate responses.

Key components

Let's start by naming and defining the components of a RAG architecture. We have deliberately limited the concepts used here to four main ones:

1. Embedding

To process natural language, texts need to be converted into numerical representations called embeddings. These vectors preserve the semantic proximity between similar concepts. Pre-trained models, such as those derived from BERT, offer a proven solution for this transformation. These embeddings form the basis for subsequent semantic searches.

2. Document Chunking

To maximize search efficiency, the corpus is fragmented into small segments called chunks. Each chunk is enriched with essential metadata (source, context, date, etc.) before being converted into embeddings. This structuring improves data retrieval and guarantees full traceability of the information used.

3. Vector databases (Vector DB)

Information identification is based on vector databases, which identify the most relevant chunks for a given question. By exploiting proximity in vector space, these databases offer optimum efficiency, even with large volumes of data. Solutions such as ChromaDB, Qdrant or PGVector are regularly used for these operations. For greater efficiency, we recommend combining text and vector approaches (hybrid model) to enhance the relevance of responses.

4. Language Models (LLM)

The extracted chunks are combined with the question asked to form a prompt. The LLM then generates a synthetic, contextualized response. This approach lightens the cognitive load of the model, enabling more relevant responses.

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RAG functional architecture

Based on these components, the architecture of a RAG system follows a well-defined sequence:

1. Corpus preparation: Documents are fragmented, then their embeddings and metadata are integrated into a suitable database (such as VectorDB).
2. Query processing: The question is converted to embedding, and the relevant chunks are extracted via a vector search.
3. Answer Summary: The selected chunks are combined with the query to create a prompt for the LLM.
4. Source transparency: The metadata of the chunks used provide an explicit list of the sources used.

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Benefits of RAG Systems

1. Enhanced reliability: The use of a controlled corpus minimizes the risk of hallucination, common in traditional LLMs.
2. Enhanced credibility: The sources of answers are clearly identified, reinforcing their legitimacy.
3. Operational efficiency: This architecture enables the use of a variety of LLMs without compromising response quality.
4. Greater flexibility: Hybrid search simplifies corpus updating, guaranteeing adaptability to changing needs.

Recent research has demonstrated the effectiveness of RAG:

• One study showed that using GAN in medical contexts improved the accuracy of radiology diagnoses by up to 54%, by enabling models to access specific, up-to-date data (source: arXiv).

• Experts have noted that RAG offers a cost-effective alternative to full tuning or retraining of AI models, allowing responses to be customized quickly without requiring significant costs (source: Big Media).

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Limits and considerations

Despite its advantages, RAG has certain limitations:

• Implementation complexity: Effective integration of RAG requires a suitable infrastructure for data retrieval and management, which can represent a major technical challenge. In particular, the integration of an infrastructure that supports RBAC (Role-Based Access Control) protocols, capable of unifying unstructured or semi-structured data in different formats, and constantly updating them, is essential in an enterprise context.
• Costs: RAG systems require considerable investment to set up and maintain. In particular, initial indexing and vectorization costs can be particularly high.
• Dependence on source quality: The reliability of the answers generated depends directly on the quality and relevance of the information sources used. It is therefore crucial to ensure that the databases consulted are reliable and up-to-date.

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Future prospects and applications

RAG systems are state-of-the-art solutions for environments requiring precision and reliability. Their integration with intelligent agents enhances their capabilities by adding additional functions, such as online search or the use of specialized APIs.

However, to guarantee their long-term effectiveness, it is crucial to evaluate their performance on an ongoing basis. Evaluation metrics should focus on the relevance of responses, security, and the traceability of information embedded in the responses provided.

In conclusion: by combining the rigor of controlled knowledge bases with the flexibility of language models, RAG architectures open up new perspectives for robust, high value-added applications. Now it's your turn!