Comparing Retrieval Augmented Generation and Large Language Models

Not All Artificial Intelligence is Created Equally

As you open your internet browser and type in your search query, you’re looking for reliable information quickly. With the rise of Artificial Intelligence, many browsers are turning to AI to offer you those quick fix answers. Some users may prefer heading to their AI platform of choice directly when they find the need for information.

While the delivery is quick, it’s the information you have to be wary of. For example, a search for the answers to “which planet has the most moons?” could turn out unsourced and outdated information. Our understanding of complex subjects evolves over time, but the internet still acts as a crypt for much of that old knowledge.

Users have become frustrated when Artificial Intelligence programs fall short on citing sources and spit out outdated info as their static training data becomes obsolete.

For companies looking to utilize AI as a means of increasing productivity this can be exceptionally frustrating. You shouldn’t have to avoid new tools due to inconsistencies with the most basic or readily available models.

Retrieval Augmented Generation can transform the way your company uses AI and eliminate the inconsistencies caused by relying on unreliable sources.

Understanding Large Language Model (LLM) AI Programs

Large Language Model AI is some of the most widely used forms of artificial intelligence. Its widespread usage includes the rise in popularity of OpenAI’s Chat GPT or Google’s Gemini.

These are typically used to generate text based on user queries.

While some of the answers generated by large language models are accurate and helpful, that is not a guarantee. They can also present you with incorrect or outdated information with the same confidence that it provides accurate data with.

Common Problems with Large Language Models

Large language AI often presents you with answers to questions without citing sources. This can lead to a dangerous spread of misinformation.

It can also lead to you receiving outdated answers. Many AI platforms are created using massive databases of training information. However, this static data can be missing the most recent information.

Even when generating answers based on internet content it can end in stale results. For example, not every news article or company website has the most recent information. If the source material is not focused, there’s no confidence in the accuracy of your generated answers.

How Retrieval-Augmented Generation Can Solve Your AI Accuracy Problems

Retrieval-Augmented Generation enhances the typical large language model process by incorporating a retrieval mechanism that sources relevant, up-to-date information before generating a response.

Primary Steps Toward Setting Up RAG

Embedding

Convert your knowledge base documents and user queries into vector representations using an embedding model.

Indexing

Store the document embeddings in a vector database for efficient similarity searching.

Retrieval

When a user query is received, find the most semantically similar documents in the vector database.

Augmentation

Combine the retrieved relevant information with the original user query to create an augmented prompt.

Generation

Feed the augmented prompt to the language model to generate a response based on both its pre-trained knowledge and the retrieved context.

Here’s How Retrieval-Augmented Generation Can Be Used

With a specific set of data, it also makes source citation easier.  RAG can generate

responses that are supported by primary source data, reducing the likelihood of hallucination and increasing credibility.

1. A user submits a query with an appropriate prefix. Prefixes will improve the quality of retrieved documents by focusing the search on more relevant information.
2. Content is retrieved using relevant information from a predefined content store. You can set this to be certain internet information, documents, or other sources.
3. The LLM generates an answer based on both the retrieved content and the user’s query.

By using RAG, you can make timely updates to your data stores. This takes the guesswork out of accuracy. You can augment your data store anytime that new information becomes available.

By relying on current, external sources, RAG improves the overall accuracy of the responses.

Lastly, when your augmented sources fail to provide proper context to answer a query, RAG can enable the model to acknowledge when it doesn’t know an answer. This will avoid the risk of misleading the user.

Making Use of RAG for Your Private Data

Retrieval-Augmented Generation (RAG) offers a powerful solution for leveraging private and dynamic data to enhance AI responses, allowing organizations to utilize large language models with their own proprietary information without compromising security or accuracy. By combining the capabilities of LLMs with custom knowledge bases, RAG enables more relevant, up-to-date, and context-aware AI interactions tailored to specific domains or internal datasets.

Integrating RAG with Vector Databases

Vector databases play a crucial role in implementing effective Retrieval-Augmented Generation (RAG) systems. These databases store data as high-dimensional vectors, allowing for efficient semantic similarity searches that are essential for retrieving relevant information to augment LLM prompts. They enable quick and accurate retrieval of contextually relevant information from large datasets, which is then used to enhance the LLM’s responses.

By integrating RAG with vector databases, organizations can improve the accuracy, relevance, and up-to-date nature of AI-generated content while maintaining the ability to handle diverse data types and scale efficiently as data volumes grow.

Experts in RAG Prepared to Help Your Business

Networking Technologies and Support is a team of IT experts well-poised to help your company solve its AI problems. Our knowledge of RAG stands out against other IT companies.

If you’re looking to improve your large language model AI usage with RAG, NTS can help set up a program that pulls answers from your predefined content database. Our capabilities include being able to create an agentic framework that helps you with AI research. We can put together a platform that leverages your private data to improve your AI research quality immensely.

Contact our team today to learn more about how your company can accurately, safely and effectively leverage AI in the workplace.