What Is Retrieval Augmented Generation and How Can Startups Use It?

Introduction

Artificial intelligence has already proven itself to be an invaluable resource for startups.

Sales teams can save up to two hours per day with AI, and marketing teams may save three or more hours on content generation tasks with generative AI. 

But AI still has its limits. AI is trained on existing data, which can sometimes be outdated or too general to provide helpful results. 

However, startups can get better AI outputs by incorporating retrieval augmented generation in their AI tools.

What is retrieval augmented generation (RAG)? 

Retrieval augmented generation is a method of combining the existing knowledge base of generative AI or large language learning models with additional specified data, such as a company’s sales playbook or content style guide, to create more accurate and higher-quality AI-generated content.

If you want to create generated content with AI, retrieval augmented generation will help ensure that the generated content sounds on-brand with your startup’s existing content and is accurate based on your company data.

How RAG works 

RAG goes through three main phases to source information, apply the information to the input, and generate an output based on the information provided. In just seconds, generative AI with RAG goes through the following steps to provide you with an output:

1. Retrieval: First, the AI must retrieve the information on which it will base content generation. It uses a retrieval model to search databases, documents, and other information for any data that may be relevant to the command or inquiry. This process can include researching external data as well as internal information specific to the company or user.
2. Augmentation: Next, the AI will work to “understand” the information to better inform its response. For example, if a website user asks a chatbot about how to troubleshoot a product, the chatbot will refer to product documentation or user manuals before developing a response.
3. Generation: Finally, the generative model sends an output that it has developed based on the data on which it was trained, as well as the additional information or data provided, such as company documents. The resulting output, which may be as simple as an answer to a question or something larger like a blog post, will be enhanced by the additional information provided.

How startups can use RAG

Many AI tools are disrupting the startup landscape by creating product mockups, generating pitch decks, optimizing GTM strategies,, and more. Many of these tools may already incorporate RAG to offer a more tailored approach to AI-powered content generation.

From providing more helpful chatbots for customers to developing personalized content for different clients, here are the many ways startups can use RAG to improve their businesses.

Customer support

Chatbots rely on RAG to provide brand-specific and even product-specific responses to website users looking for answers to their questions. RAG-powered chatbots can pull data from company documents and previous customer interactions for higher-quality responses to user questions.

AI chatbots are helpful for startups that may not have the resources to answer every question that comes through the website. Rather than having customer service reps live chat with every website visitor who uses the chat function, a chatbot can handle some of the simpler inquiries, reserving more complex problems for the human reps. However, with RAG, chatbots will continually improve their responses, so only the most complex inquiries need assistance from a startup team member.

App development

For startups who want to develop an app to complement their products or services, RAG can improve the app by learning from the company’s existing internal database. A startup app can also help with internal operations. In either use case, RAG improves apps like chatbots or team communication platforms by turning to internal data, in addition to pulling information from external sources, to provide more accurate data that is more relevant to the context of the input, such as an inquiry or content generation command.

Content creation

Whether you need to produce blog articles for a website or social media captions, generative AI is a helpful tool for startups with limited team members and finances. However, by using AI as-is, startups may find that the resulting content isn’t quite relevant or is somewhat outdated.

RAG allows the AI to search your company’s internal information to get a better understanding of tone, style, factual information, and other details when creating content. That means the generative content will be creative while still being accurate and a good representation of your brand. 

Using RAG for content creation can also lead to huge time savings. For example, Mark Kaput, Chief Content Officer at Marketing AI Institute, uses generative AI for aspects of podcast production. By using well-trained AI, Kaput and his team save about 75% of the time they used to spend on making the podcast, Kaput told HubSpot.

Search and recommendation systems

There’s nothing more frustrating than using a website search function only to have it spit out completely irrelevant content related to the search term. This experience can aggravate website users who are searching for specific products, services, or content. RAG can improve search results and recommendations by understanding the input (or search term) and retrieving the most relevant results. 

Not only can RAG improve the accuracy and relevancy of search results, but it can also provide generated explanations, descriptions, or summaries that make the search results more helpful to customers. RAG search and recommendation systems can be especially helpful for e-commerce or media startups.

Research and development

The research and development process requires developers to source information from huge databases, which can be time-consuming and challenging to keep the information organized and easily accessible. Enter RAG, which can quickly organize and summarize information from the provided databases.

RAG saves R&D teams time and frustration in sorting through massive spreadsheets or other databases, and it can speed up the process of making development decisions. For startups, a faster R&D process can be the difference between getting to market ahead of the competition or arriving to market too late, when demand has passed.

Personalization

Personalization is one of the top ways that startups can stand out from competitors and offer a better user experience. According to McKinsey & Company, companies that use personalization can increase revenue by around 5% to 15%, and customers may feel more loyal to brands that offer personalized experiences. About 76% of customers may even feel frustrated if brands don’t offer personalization.

To make the personalization process quicker, RAG can retrieve user-specific data, like browsing history or past purchases, to offer exclusive deals, discounts, recommendations, and other content.

Benefits of RAG

Generative AI has become a time- and money-saving tool, especially for resource-strapped startups. RAG increases those benefits further by offering more customization and control over the AI. Ultimately, this leads to better AI interactions for both startup employees and customers.

Improved outputs

RAG is a boost to standard AI. While startups can find pre-trained language learning models (LLMs), these options won’t offer brand-specific outputs. Whether for search results or chatbots, that means the results won’t be as helpful or relevant to the company. More general or generic results can lead to a more frustrating user experience, whether that’s for startup employees or customers.

RAG offers a customization element to produce better, brand-specific outputs. The more data the company provides the LLM over time, the better the outputs will be.

More control

Out-of-the-box LLMs are limited to the information on which they were trained, which can impact the accuracy or relevancy of results. RAG allows companies to customize their AI tools and continuously update them with company data. Company developers have more control over how the LLM is learning and updating, which will also lead to improved outputs.

Better customer experience

With more accurate and relevant outputs based on specific company data, RAG-powered tools improve the entire customer experience. Customers can get relevant answers to their questions in a chatbot, company-specific documentation for self-service, personalized offers and recommendations, and improved search descriptions and summaries. 

HubSpot uses RAG to integrate Academy resources with ChatSpot and in-app help widget 

HubSpot’s engineering team used RAG to improve its ChatSpot chatbot and the help widget inside the HubSpot app. HubSpot has an extensive knowledge database within HubSpot Academy, but users may not always know where to go on their own to find the information they need.

By integrating the data available in the HubSpot Academy, the app’s help widget and the ChatSpot tool can now offer more precise responses to customer inquiries. When a customer submits an inquiry, these tools are trained on HubSpot Academy information and will retrieve this data, which includes over 700 hours worth of content, when formulating a response.

This process means that users will now get up-to-date, informative, and accurate information directly from HubSpot Academy to answer their questions. This improves the customer experience while empowering self-service, which can also save time for customer service reps.

Improve AI tools and empower your users with RAG

AI is a powerful resource that saves startups time and money as they scale. However, the AI tools of today still have their limitations in providing accurate, relevant outputs, and they may have knowledge gaps that lead to inaccurate or invented results.

By using RAG methods, startups can train their LLMs better and get a more customizable tool that is tailored to their businesses. The result is an ever-improving LLM that offers on-brand, personalized, and accurate results in seconds. All in all, using RAG can improve the customer experience, boost customer loyalty, and lead to better revenue outcomes.