Revolutionizing Workflows: How AI and RAG Boost Business Efficiency

Photo by Dylan Gillis on Unsplash

TLDR: This article delves into the practical application of artificial intelligence in business, moving beyond the initial hype to demonstrate how generative AI and large language models (LLMs) can enhance efficiency and productivity. It explains the fundamentals of LLMs, highlights common challenges such as hallucinations and data limitations, and introduces Retrieval Augmented Generation (RAG) as a powerful solution to integrate proprietary knowledge, reduce errors, and provide accurate, context-specific responses. The piece also outlines the business benefits, including faster knowledge retrieval and increased trust in AI tools, while discussing implementation overhead and key considerations like data security for successful adoption.

Managers looking to make a splash used to announce that they were adding AI to their business unit and workflows. AI used to be a lot of hype and some misdirection, but it is now a practical and real way to improve efficiency and output.

Integrating AI Chatbots, AI agents, and workflows in your business can automate tedious and redundant processes, freeing your team to focus on what improves the bottom line. There are some drawbacks to using AI in business. Luckily, tools like RAG (retrieval augmented generation) can help minimize issues when using generative AI.

What is an LLM, and what kind of AI is it?

AI, or artificial intelligence, is a blanket term that covers many different technologies. Some everyday products and technologies that use AI include:

• An Amazon Echo that tells you the weather
• A self-parking Tesla
• A bank app that allows you to scan and deposit a check
• A chatbot widget in an online store that helps you return a blazer

AI applications are wide-ranging and embedded in many industries and goods. Some of their categories include generative and predictive, among others. For our business case, we are going to discuss generative AI.

Generative AI has become popular as a consumer-facing application. AI products such as ChatGPT, Grok, and DeepSeek use generative AI. These products utilize LLMs (large language models) and UI wrappers to deliver a solution for fulfilling user queries with AI.

Generative AI does not know what you are asking or the response it provides. It uses complex mathematics and statistics to generate weighted guesses of the correct response to a user query. There is no machine understanding, only machine calculation.

LLMs are models trained on large datasets consisting of millions or billions of words (parameters). They use this data to mimic available language patterns and weigh the most likely responses based on inputs provided by users.

The base technology of LLMs is more similar to the word predictor in Microsoft Word or your smartphone’s messaging app than the polished chatbots provided by ChatGPT or Grok.

Figure 1. Example of what LLMs are doing as stream of conversation predictors (image credit https://blog.tobiaszwingmann.com/p/how-large-language-model-chatgpt-really-works)

These products add a program layer that structures LLM logic to mimic a conversation instead of a stream of conscious words:

Figure 2. Example of a conversation with ChatGPT (credit: chatgpt.com)

Generative AI products are helpful for specific applications. Summarizing emails or significant texts, generating code snippets, and outputting overviews of articles. They don’t think, but rather compare the “meaning” of the provided input and rank it, and then return a relevant response by weighing the training data against the provided query.

To further understand this product, we can break down the GPT in ChatGPT. GPT stands for a generative pretrained transformer. The tool leverages a pretrained dataset to transform weighted values and generate a coherent response for users.

This technology is powerful for all users, from hobby consumers to multinational businesses. The number of novel applications of generative AI is enormous; however, there are issues associated with using these tools.

What are the common issues of LLMs and generative AI?

Generative AI and LLMs provide much value to users. From generating a topical team name for trivia to generating code for a widget, the use cases are endless. Similar to other technologies, generative AI has its issues.

One of the most prevalent issues with LLMs is an output known as a hallucination. Generative AI chat apps are always going to return a response, answering with authority. Researchers describe hallucinations as plausible but incorrect LLM responses. Just because you get a response does not mean that the provided response is valid.

Returning to the explanation of what an LLM is from the previous section, an LLM can only answer questions based on the data it was trained on. Early ChatGPT used training data up until 2021. Ask ChatGPT who won the 1918 World Series, and it would answer the Red Sox. However, ask it what the weather is like today in Boston, and it would not be able to give accurate data due to the 2021 time frame limitation.

Consumer LLMs are not trained on proprietary knowledge and lack data, similar to the 2021 cutoff issue. Consumer LLMs are built by scraping internet data, rather than internal documents, numbers, and other sources. If you were to ask it about the number of holidays taken in June 2024 for your engineering team, it would have no idea. It has no access to this data.

The problem of hallucinations cannot be solved entirely, as we don’t fully understand LLMs and neural nets. However, with the advent of function calling and internet access, many models have overcome the 2021 time cutoff issues. With technologies like RAG, you can solve hallucinations due to the LLMs not having access to your data.

What is RAG (retrieval augmented generation)?

The lack of domain-specific business logic, as well as the possibility of hallucinations, is a cause for concern for business managers looking to add AI to processes. Luckily, there are tools and methods to mitigate some of these concerns. When it comes to generative AI, one of the best solutions for preventing incorrect responses is using retrieval augmented generation (RAG).

RAG is a process used to supplement a base LLM’s knowledge with company and process-specific knowledge. It combines the generative power of an LLM with a data store of proprietary or non-public information to create accurate responses.

Training an LLM for your specific business unit can be costly, as it requires large amounts of training data (parameters) and knowledge (a data scientist or AI engineer). With RAG, you can use an off-the-shelf LLM and your existing documents to create and return generative AI responses based on your internal company knowledge.

How does this work? Let’s break down RAG. Below is a diagram of how the RAG process works with generative AI Chatbots:

Figure 3. Process flow for basic RAG (credit https://blogs.nvidia.com/blog/what-is-retrieval-augmented-generation/)

There are two main components to understand RAG:

1. Making your data accessible: Transforming your existing internal documents into a vector store
2. Incorporating your data into responses: Augmenting user queries to the chatbot with vector store records

The first step in understanding RAG is transforming internal documents, such as text documents, financial records, meeting audio recordings, and slide decks, into data that LLMs can utilize. Any data source that you can convert to text can become a reference for augmenting your AI responses.

We need to transform language-based data into vector records, which are a collection of decimal numbers between 0 and 1. LLMs don’t speak text; they speak in tokens in vectors.

To get these values, we will perform the following steps:

1. Chunking and Overlapping — breaking up bodies of text and language into processible, meaningful segments
2. Embedding — converting chunks of text into vector records
3. Writing to a vector database — storing vector records in a data store

This process is much more involved, but for the scope of this article, we will stop here. If you’d like more in-depth knowledge, there are plenty of blog posts and articles by IBM, Nvidia, LangChain, and OpenAI that describe the transformation process.

Figure 4. LangChain article on more in-depth RAG (https://blog.langchain.com/tutorial-chatgpt-over-your-data/)

Now your data is in a format LLMs can query and retrieve. The second part of the diagram is what to do with the user query. The process for using RAG with the query is as follows:

1. User makes a query in the UI of the AI chatbot.
2. The LLM tokenizes the query and creates embeddings.
3. The LLM makes a call to the vector store to compare the values of the query to vector records in the database.
4. The vector database returns matching records.
5. The LLM uses the user query and the returned records to generate a response to the user (open source LLM knowledge + proprietary vector datastore knowledge).

From these steps, we can now understand the parts of RAG. Retrieval refers to the process of retrieving data from outside our base LLM model to create a response. Augmented refers to using this data to change the initial response from what the LLM would provide. The generation part is creating a response output for the user that uses the LLM’s body with the knowledge of the retrieved documents.

Using RAG allows you to get value from existing internal documents and turn your AI Chatbot into an expert on your business subject. It is a great way to extend the abilities of off-the-shelf LLMs and reduce hallucinations in responses.

What are the business benefits of using RAG in your AI Chatbots?

We have talked about what LLMs are, the pitfalls of generative AI, and how to alleviate these issues with RAG. Making a change to your process flows requires more than just lower miss rates on queries. What are the other business benefits of generative AI and RAG?

Before we discuss the benefits, I want to stress the importance of having robust and versatile data pipelines. As mentioned earlier, any data that can be represented as text can be ingested into a vector store or LLM. Your articles, slide decks, emails, audio recordings, meeting videos, etc, can all be run through AI or other software to produce text-based representations.

This ability is massive for making informed decisions as a manager or business leader. Imagine having a conversation with your inbox to find a figure from an RFQ received four months ago, instead of struggling with search and filters to access the document.

Figure 5. Microsoft Outlook inbox. Could use RAG integrations to search inbox through AI prompts.

Strong data pipelines will allow you to pull and translate any data sources into vector datastore records. Once these are in place, you can quickly leverage any data that may be useful to you. Let’s talk about some of the benefits of adding RAG-powered chatbots to your business and the direct impacts you will see.

The first, and most significant benefit of using RAG with AI Chatbots is access to proprietary information. With RAG, you can leverage existing data sources in any query your users have. RAG allows you to have informed responses without exposing your data to base LLM models.

Second, you will get a higher match rate for searching documents and quicker knowledge retrieval. Most LMS (learning management systems) used for storing SOPs and training use a basic tagging system. A document can belong to a main category, say human resources, and have further tags of a year and an owner.

These tags can help users find documents more quickly than if they had to review all records in the LMS; however, it is not the fastest way to obtain answers to internal questions. Good embeddings for data vector stores include both the chunked data and a pointer to the source document.

RAG is quicker at taking a plain English question and returning a response with document attribution. With this, you can get back both an answer to your question as well as the document it pulled from for verification and further review.

Figure 6. Source attribution by LLM (source of image is Grok).

This quicker lookup and document search will lead to higher usage of existing resources. You can recoup your initial investment in creating document sources and seeding LMS systems with knowledge by encouraging your workers to utilize them more effectively. Better, quicker, and easier access to data has a flywheel effect, enabling workers to use these resources more frequently.

Finally, RAG increases trust in your AI services. Hallucinations will still happen. There is no way to eliminate hallucinations due to the nature of generative AI. However, with RAG, you can decrease the likelihood of hallucinations while also providing tools for user mitigation of hallucinations.

As discussed, RAG allows you to include access to your proprietary knowledge, which decreases hallucinations. Along with the AI-generated response, you can use the embedding’s metadata of the chunk source to provide the user with context on where the knowledge was pulled from. Attribution enables users to verify that the source was relevant and that the data was correct.

What kind of overhead is there for adding RAG to your business team?

You may think you need to add RAG to your business processes and AI tools. It all sounds great, so what’s the overhead or cost to you? There are three steps to implementing RAG into your current LLM products:

1. Create data pipelines to connect your current data and documents to AI apps
2. Create processes to chunk data, create embeddings, and store vector records in a vector database
3. Implement looping calls with your existing LLM process to perform RAG calls for user queries

If you have data engineers, you are already on track to create the necessary data pipelines. You may require an AI-focused software engineer to handle the final two points of transforming data into vector records and implementing RAG with your existing LLM products.

If you are already using generative AI chatbots in your current workflows, then the overhead is not huge. If you have no AI infrastructure, then the overhead will be significantly higher.

Conclusion

AI in business has evolved from a flashy buzzword aimed at gaining attention from managers to a tool that can have a positive impact on teams and business units. It is not a magic wand to wave and fix flawed businesses, but it can boost productivity and performance in some instances.

We have discussed the benefits of generative AI in productivity boosts and asset utilization. Technologies like RAG (retrieval augmented generation) help curb some of the risks associated with proprietary knowledge moats and LLM hallucinations.

While this technology is excellent for training LLMs to be subject matter experts, there are more considerations when implementing RAG and chat-based AI for your business. RBAC permissions (role-based access controls) are essential to limit sensitive documents to only authorized users. These are not easy to implement currently, as an LLM is a black hole for business knowledge, and it is hard to train models to return specific knowledge to certain users.

Systems will get easier to manage and navigate. Still, for now, your company and teams can see an immediate boost by using RAG on internal documents and procedures to increase utilization.

Key Takeaways

• AI has transitioned from a buzzword to a tangible tool for automating repetitive tasks and improving business output.
• Large Language Models (LLMs) power generative AI by predicting responses based on vast training data, similar to advanced word predictors, but they lack true understanding.
• Common LLM issues include hallucinations (plausible but incorrect outputs) and limitations from outdated or non-proprietary training data.
• Retrieval Augmented Generation (RAG) mitigates these problems by combining off-the-shelf LLMs with company-specific data stored in vector databases, enabling accurate and relevant responses.
• Business benefits of RAG include access to proprietary information, quicker document searches with attribution, higher utilization of existing resources, and enhanced trust through reduced hallucinations.
• Implementing RAG involves creating data pipelines, embedding processes, and integrating with existing AI tools, with overhead varying based on current infrastructure.
• Future considerations emphasize role-based access controls (RBAC) to secure sensitive data when using AI in business environments.