The emergence of Large Language Models (LLMs) was a major business operation and strategy breakthrough.

These AI-powered models' capabilities make it possible to work in unprecedented ways, as they can recognize, analyze, and generate content based on the data they were trained on.

Retrieval-Augmented Generation (RAG) takes these capabilities to the next level by retrieving data in real-time to generate content. Essentially, RAG is an evolution of the LLM, as it significantly improves its generation capabilities without the costs and complexities associated with training a custom model.  

In this article, we will explore the applications, mechanics, and underlying process of RAG and unveil how it is shaping the future of AI-powered solutions by making content generation more efficient than ever before.

‍

What is RAG?

Retrieval-Augmented Generation (RAG) is a technique that enhances the output of LLMs by injecting external information into the model’s prompts. With RAG, LLMs retrieve information from structured data – databases, spreadsheets, CRM systems – and unstructured data, including documents, emails, or web content. This approach enables LLMs to access domain-specific knowledge and generate accurate, context-specific responses without requiring expensive and time-consuming training processes. 

Businesses use LLMs in one of three ways, each coming with their unique strengths and trade-offs: 

1. Zero shot

The LLM is used as it was released by the provider – relying solely on the general knowledge it was trained on. This approach is cost-effective and easy to implement but limited with no evolution possibility. As such, the model struggles to answer queries about specific company or industry-oriented data or niche use cases. 

2. Fine-tuning

By training an LLM on a specific dataset, businesses can customize models to their needs. However, this process is resource-intensive because it requires high-quality data and weighty computational power. 

3. RAG

RAG offers an efficient alternative to the other two methods. Instead of training a model, business users can “inject” information into prompts so that the LLM can “reason” over it. This approach makes it possible to fetch information from external sources so that the LLM can summarize or generate content with more accurate, updated material. In other words, it’s a win-win solution that is both cost-effective and context-specific.

Key components of RAG: retrieval

Essentially, a RAG architecture is made of three core components: retrieval, re-ranking (optional), and generation. 

In the retrieval phase, the system identifies and fetches the most relevant information from external data sources, such as databases or unstructured text. This process is supported by a vector database, which organizes and retrieves data through vector representations. 

Data is first stored and processed through an embedding model, usually smaller and more specialized than an LLM, which converts data into vectors – mathematical representations in a high-dimensional space – where similar data points are close together. 

Example: 

If we embed two books on related topics, like “desktop computers” and “laptop computers”, their vector representations might look something like this: 

• Book A (Desktops): 1, 1, 1
• Book B (Laptops): 1, 1, 2

The small distance between the vectors (typically cosine similarity) indicates that their content is very similar. Real-world examples are much larger, and the embedding models use up to thousands of dimensions (and not 1, 1, 1) but the principle remains the same. 

During the retrieval process, the user’s question is embedded into a vector using the same embedding model used to populate the database with data. This makes it possible to retrieve relevant information to inject in a prompt through a vector search. 

Example: 

Let’s say a user in the banking industry wants an answer to the question “What are the current mortgage rates?”:

• The query is embedded into a vector representation with the same embedding model. 

• A vector search is performed to detect the closest matches in the database. 

• The system returns the top-K closest vectors, ranked by similarity. If K (the parameter) is 3, the vectors may identify matches that come close to the initial question, such as “current mortgage rate is 3% for 20-year term”, “variable mortgage rates start at 3.5% depending on credit score”, or “as of September 2024, mortgages rates have risen by 0.1%.” This information will go to the prompt, allowing the LLM to reason over it and deliver final results for the user. 

Remember, to build an effective retrieval system, you must first establish which embedding model is better fitted, and how to properly embed large documents (like books, which requiring preprocessing and chunking) and unstructured data (like PDFs or images, which involve a more complex pipeline). You should also select the appropriate distance metrics. 

‍

Key components of RAG: re-ranking and content generation

Although the retrieval phase is fast, it is not always accurate or effective because the algorithms operate on approximation. As such, they may identify the nearest points but the results are often not sorted by distance. Converting text to numbers (books to vectors) can lead to information loss. The re-ranking phase is therefore necessary to refine and reorganize information to ensure answers to queries are as relevant as possible. 

This optional phase cannot be part of the retrieval process because the vector database is very large (it could sometimes include millions of data points). Therefore, breaking down the answer delivery into two separate phases helps provide more accurate answers. The retrieval phase narrows down the dataset, and then the slower but more accurate re-ranking process refines and selects the best candidates for the user’s request. 

Finally, the LLM uses the retrieved and re-ranked information to generate responses that are both context-specific and grounded in facts.

‍

Reference architecture for RAG applications

Implementing a RAG system requires a well-defined, high-level architecture with the following components: 

1. Data sources

Structured and unstructured data, to create a foundation of information. 

2. Vector database

Information converted into embeddings and stored in a vector database, which includes embeddings and metadata for the retrieval process.

3. LLM inference service 

A service that processes the input prompt, retrieving the best K documents from the vector database to provide context-specific information, and allowing the LLM to provide responses generated with the augmented prompt. 

‍

‍

RAG applications in business

RAG’s sophisticated architecture and retrieval-generation mechanism support business users leveraging relevant insights to inform future decisions and actions. Subsequently, it enhances all areas of a business organization relying on accurate information, from strategic planning to market analysis, as in the following examples: 

1. Customer support and virtual assistants

RAG enables virtual assistants and customer support applications to fetch and deliver company-specific information, which makes results more personalized and accurate, and ultimately improves customer satisfaction. Moreover, RAG can infer answers from information external to the LLM during runtime, meaning the knowledge remains separate and isn’t permanently stored in the LLM. This approach ensures better compliance with privacy requirements, especially when the LLM operates on-premise, without sending sensitive data to external servers.  

2. Content generation and summarization 

RAG can automate content generation of industry reports, product descriptions, and content moderation, ensuring that outputs are consistent and aligned with the latest data. 

3. Decision support systems

RAG optimizes decision-making by helping managers retrieve relevant data and generate actionable insights, which in turn adds value to strategic planning or market analysis. It is particularly useful for Decision Intelligence platforms. 

‍

Benefits of RAG for businesses in regulated industries

RAG offers several advantages for businesses looking to use an LLM for their operations. Of course, it is a fast, cost-effective solution, but it is also particularly beneficial for regulated industries demanding maximum data security. Using RAG avoids training sensitive information directly into the LLM, which protects companies’ data and copyrighted material. As such, organizations in regulated industries can trust that their material will stay private and won’t be exposed to confidentiality breaches.

Moreover, RAG architecture can handle vast amounts of business data, making it a better fit for companies with extensive data repositories. While integrating multiple sources can add complexity, the system remains scalable. However, the simplest scenario involves a single data source for each use case, where information is processed and stored. 

‍

Challenges and considerations

Despite its numerous advantages, RAG still presents a couple of challenges. Here are some of the elements to consider before adopting this method: 

1. Capacity and scalability

Integrating various data sources into a RAG system can be complex, especially when handling several large or heavy datasets. Scaling a RAG system to accommodate new sources requires implementing a new pipeline, updating the vector database, and ensuring compatibility with the existing structure. This can be complex and challenging for businesses needing to adapt their system. Moreover, due to the limited context size of LLMs, large documents must be divided into smaller chunks, which requires careful planning to maintain coherence and relevance in the retrieval process. 

2. Data maintenance

Any information that is either obsolete or irrelevant will contribute to inefficient output. Therefore, regular data maintenance and updating is crucial to maintain the high-quality level of the retrieved information and avoid inaccuracies. 

3. Access and privacy control

One of the major concerns in deploying RAG is the potential for data privacy and security breaches, especially when retrieving information from sensitive sources. The RAG system should only retrieve data that the user is authorized to view. So, even if certain information is available in the database, it should remain visible only to the interested party and not be delivered to an unauthorized user. 

‍

How your business can deploy RAG

1. Latency considerations

Before deploying RAG for your company, you’ll need to assess the latency of the system – the time between  a user’s request and the answer delivery, which depends on how many data components are in the pipeline. The lower the latency, the quicker the responses. While retrieval is usually fast, the re-ranking phase is slower. However, you can balance accuracy and speed by choosing to limit the number of documents optimized by the re-ranking phase, which is always optional. 

2. Data integration 

Consider the seamless integration of multiple data sources into the vector database. The system should integrate the information injected while getting the opportunity to divide it logically to respect privacy constraints and ensure only authorized information is accessible. 

3. System maintenance 

Keeping the vector database refreshed and up to date is vital to maintain accuracy and relevance. Make sure your system is monitored to detect any malfunctions or anomalies, including in the re-ranking model, and address any issues as fast as possible. 

‍

Beyond RAG applications: future trends

Looking ahead, RAG is poised to evolve further, with more capabilities for optimized and personalized response options. Specifically, refining the precision of retrieval and re-ranking processes will remain a priority, with plans to optimize algorithms and ranking models, for a more accurate identification of relevant documents within tighter timeframes. 

Additionally, improving capacity will boost the system’s overall output quality and efficiency. For example, enlarging the context window will allow it to incorporate more documents in the input prompt, adding more depth to the system’s responses. Meanwhile, advances in chunking strategies will break down larger documents into meaningful segments, which would maximize the utility of the information provided to the LLM. 

‍

For data-intensive industries, RAG remains a solution of choice, as it ensures responses grounded in updated information without the cost of training and fine-tuning an entire LLM. It is a compliant solution for regulated industries, as it generates informed responses without embedding sensitive data into the model. 

However, privacy challenges remain, as measures are needed to prevent unauthorized data access during inference. Companies requiring maximum security for their Decision Intelligence can opt for specialized solutions, such as Unicorn, which incorporates RAG into private and personalized custom models, ensuring complete data protection and safety. 

One thing is certain, RAG stands out as a cost-effective and practical solution, paving the way for the future of advanced AI architecture. 

Is your company ready to harness its power to shape a smarter future? 

‍