Boosting LLM Performance with RAG

What is RAG?

Retrieval-Augmented Generation. It is a technique that increases the accuracy and reliability of LLM models by feeding relevant and specific information or data sources to the model. With this system, users can build conversations with data sources. For example, a finance analyst can take assistance from this RAG system to find insights from a company report by simply questioning the system.

In fact, all businesses can turn their technical or policy manuals or QA sections into knowledge base resources and provide them to the LLM, by which the LLM works more efficiently for that particular domain.

Let’s understand RAG with one example from a business perspective. Let’s consider a real estate business. For legal documents, we are picking two scenarios—one without RAG implementation and another with RAG implementation.

User Asked (buyer or agent): Can you explain the legal obligations of the buyer mentioned in this property sale agreement?

Without RAG:

If the business has not implemented RAG for this and totally depends on pre-trained data, then the model may generate a generic response or hallucinate that may not be real due to knowledge cutoff.

With RAG:

In this case, the chatbot is built using RAG and we feed legal documents of the property. Then the chatbot provides accurate, clause-specific answers, reduces the risk of hallucinations or generic replies, and saves time for legal professionals or buyers.

With this RAG-powered system, the chatbot will give a response like:

Chatbot:

“As per Section 4.2 of the sale agreement, the buyer is obligated to pay the remaining 90% of the property value within 30 days of signing and must complete registration within 45 days.”

How RAG Works Here

1. Understanding of query:

   Here, the user query is understood by the LLM about obligation of a buyer for perticual property

2. Retrieval of data:

   Then it fetches matching data from the provided resource knowledge from the internal database of current listings, property brochures, past chat logs or FAQs, and PDFs with rental terms.

3. Augumented Generation:

   With the retrieved data, we can generate an accurate and conversational reply to the user by using the LLM.

These are the high-level workflows of the RAG system. Now, we will further discuss how we can build a RAG system and what its components are. So, let’s start…

To better understand the architecture of RAG, we break it down into two parts.Before that, let’s understand how we are giving our data source—let’s suppose PDF in this case—to the LLM as context.

There are two options to provide context data to the LLM. First, we can directly convert the PDF to text and give it to the LLM as a system prompt context. This will work fine and also satisfy our use case, but the main issue with this approach is that the LLM has a context limit and PDFs may contain a large amount of text.

The other one is to break down that PDF into chunks and store its embeddings into a vector DB. At the time of a user query, we find similarity in the chunked data and only pass relevant data to the LLM.

Let deep dive into this…

1. Injection Process:

   

   In this approach, we begin by collecting raw data from sources such as PDFs, Word documents, and Excel sheets. We then break this large dataset into smaller, manageable chunks. Each chunk is converted into embeddings using an appropriate embedding model and stored in a vector database. This process enables efficient retrieval of relevant or similar information based on user queries.

2. Querying Process:

   

After storing vector embeddings in a vector database, we effectively structure our knowledge base in a way that allows us to retrieve only the most relevant information based on user queries—without needing to pass large amounts of data to the LLM context.

Let’s take a real estate legal document example to understand this better. Suppose we’ve already processed and embedded a large collection of property legal data and stored it in a vector database. When a user submits a query like “Can you explain the legal obligations of the buyer mentioned in this property sale agreement?”—this query is also converted into an embedding using the same model that was used to process the original data. We then perform a similarity search in the vector database using the embedded query, which returns the most relevant chunks of information.

These relevant chunks—where the actual content of each chunk is stored in text format within the metadata—are retrieved and passed to the LLM along with the original user query as plain text context. This enables the model to generate precise and highly relevant responses based entirely on the structured knowledge we’ve provided, ensuring accuracy, efficiency, and contextual relevance.

This approach not only enhances the performance of the LLM but also makes the system scalable and resource-efficient for real-world applications.

Conclusion

RAG (Retrieval-Augmented Generation) offers a powerful solution to overcome the limitations of traditional LLMs by combining the capabilities of retrieval systems with generative models. By integrating external knowledge sources—such as PDFs, Excel sheets, and databases—into the response-generation pipeline, RAG ensures that responses are not only accurate but also grounded in up-to-date, domain-specific data.

Through the use of embeddings and vector databases, RAG allows systems to intelligently fetch only the most relevant chunks of information and feed them into the LLM. This makes it possible to generate context-aware responses without overloading the model with unnecessary data.

Whether it’s real estate, finance, healthcare, or any other domain, RAG empowers businesses to transform static documents into dynamic knowledge bases—enabling more intelligent, reliable, and efficient interactions.

As AI continues to evolve, building systems with RAG will be a critical step toward developing smarter, more specialized applications that bridge the gap between raw data and meaningful insights.