Leveling Up Your LLM with Retrieval-Augmented Generation

By Garry Klooesterman | 2025 Apr 10

6 min

Introduction

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Generative large language models (LLMs) are popping up everywhere it seems. More and more businesses are turning to AI solutions like LLMs to help streamline their operations, provide better customer service, boost productivity, reduce costs, and more. However, traditional LLMs come with their own challenges like hallucinations and working with outdated data, to name a few. Relying on traditional LLMs alone can produce undesired or inefficient results. For example, in a previous working life, I would routinely be asked to provide a list of all pages on our site that contained a specific term, such as “prepayment penalty.” With limited tools at my disposal, the process would take hours to comb through thousands of pages, spitting out a long list that included irrelevant links such as outdated communications or matches that were out of context. This experience highlighted the need for a solution that goes beyond simply identifying and retrieving a specific term by also understanding its context—which is an issue for traditional LLMs on their own.

To tackle these challenges, businesses are integrating Retrieval-Augmented Generation (RAG), transforming their LLMs into a more powerful solution with many benefits, including contextual understanding.

This blog will define RAG and discuss how it works along with pros and cons, use cases, and how to get started with a document processing SDK such as the Apryse Server SDK.

What is RAG?

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Retrieval-Augmented Generation is an AI-framework that allows LLMs to access external data, building on the training data instead of having to retrain the LLM. Think of it like a plug-in for your favorite app.

RAG optimizes LLM output by combining traditional information retrieval systems such as search and databases with the power of LLMs, meaning, questions can be answered more accurately as there is more specialized knowledge to draw from. It is also a more cost-effective way to improve the output of LLMs by maintaining accuracy and relevancy.

How Does RAG Work?

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Let’s look at a simplified explanation of how RAG works.

Processing External Data: External data, outside the training data, is broken down into smaller portions and assigned numerical representations of meaning, known as vector embeddings, and stored in a vector database to create a library of information LLMs can understand. This external data can come from multiple sources with documents being among the most common. Other sources include databases, repositories, and online content such as news articles, websites, and more.
Retrieving Relevant Information: Now that the model has a deeper data pool to draw from, it needs to find the most relevant information by performing a relevancy search. The model converts the user’s input to a numerical value, just like the information was previously, and looks for entries in the vector database that were calculated to have a high relevancy. For example, if a user’s input involved a particular regulation such as Foreign Account Tax Compliance Act (FATCA), the model would look for documents with a high relevancy to FATCA.
Generating Output: Now that the RAG model has processed the user’s input and retrieved content with a high relevancy, it uses prompt engineering to modify the user’s original input by adding the retrieved data. This modified version is then passed to the LLM, which is now able to generate a more accurate answer based on the additional data and context.

The Pros and Cons of RAG

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Implementing RAG, as with any technology, comes with its pros and cons. Let’s look at a few of the top ones from each side.

Pros

Efficient Token Usage: Tokens are representations of the data set such as a sentence broken down into chunks containing words, phrases, or even single characters. Tokens are consumed by various components including the input prompt, chat history, retrieved information, and the generated response, with information retrieval (especially whole documents) usually taking the most tokens. RAG reduces token usage and costs by retrieving only smaller, more relevant parts of a document.
Dynamic Information Retrieval: Since RAG works dynamically, there’s no need to reprocess entire documents, making it ideal for interactive use cases such as chatbots or follow-up queries.
Hallucination Reduction: RAG models are less likely to create false information, or hallucinate, as only the most relevant data is provided to the LLM with the output being grounded to that data.
Reusability: Storing vector embeddings in a database for future use allows for faster retrieval and is ideal for iterative or long-term use cases.

Cons

Complex Set Up: The complexity of setting up RAG depends on various factors including the number of external data sources and requiring additional infrastructure such as a vector database and a document processing pipeline.
Variability in Performance: As the model’s output quality relies on the effectiveness of the retrieval system, other strategies may be necessary to optimize the results. For example, semantic reranking uses AI to reorder the search results based on their semantic similarity to the user’s input.
Suitability: RAG may not be ideal for one-time or short-lived use cases as factors such as set up costs, implementation complexity, and maintenance could outweigh the benefits.
Implementation Knowledge: Implementing RAG requires significant knowledge and familiarity in various areas such as connecting to outside data sources, vector embeddings and search, document processing pipelines, and more.

Use Cases

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LLMs are useful in many industries with use cases including content creation and chatbots for customer service. RAG takes an LLM to the next level by expanding the data set available to the LLM and by how it processes the user input and the retrieved data giving the LLM a more accurate and grounded base to use when forming an answer. Let’s look at a few use cases where RAG stands out.

Chatbots: RAG enhances a standard LLM chatbot by providing access to real-time information for improved accuracy and relevancy of the responses.
Document Summarization: With documents being broken down into smaller portions and assigning vector embeddings, only key information is extracted without having to process the entire document, regardless of length.
Knowledge Base Retrieval: Retrieving only data with high relevancy to the user’s input enables faster and more contextually relevant responses.
Financial Industry: Financial institutions can use RAG to quickly process millions of transactions to look for suspicious patterns and other signs of fraud.

Getting Started

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We’ve covered the pros and cons of RAG and how it can transform a traditional LLM into a more robust solution. But how do we get started?

The first step is processing your external data (data outside the training set) and building a custom dataset to be used by the LLM. Since external data may be in various formats, including PDFs, extracting the data into a high-quality, structured format is essential for it to be processed effectively and efficiently by an LLM enhanced with RAG. PDF documents often contain complex structures, such as embedded images, tables, and multiple layers of text which can make it difficult to extract data. Using a document processing SDK, like the Server SDK from Apryse, ensures the data is extracted accurately to a structured format.

The Apryse Server SDK includes advanced features such as optical character recognition (OCR), text extraction, table recognition, and image processing to effectively extract data from PDFs to use in building custom datasets. The SDK enables users to customize their data extraction process to create specialized datasets, which can improve RAG model performance.

For more information on RAG and setting it up, see our documentation.

Conclusion

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RAG empowers businesses to transform their LLMs to the next level by enabling much more relevant responses by grounding the answers to an expanded pool of specialized data. To transform even your most complex data, Apryse offers a robust document processing SDK to create the high-quality, structured dataset you need.

Want to learn more, contact us today.