What is RAG and how does it strengthen and improve AI automation?

Natalie Smithson
AI enthusiast | Tea addict | Focused on using AI assistants to win the working week
Illustration of a man sitting at his laptop uploading documents to an AI assistant on another computer system

Why are you hearing the term RAG everywhere in AI?

We’ll tell you…

Find out what RAG stands for, what it is, and how it improves enterprise AI. By the end of this post, you’ll understand how you can use RAG to improve your automated services, take the pressure off your teams, transform your customer insights, and make automated experiences a breeze for your customers.


  • RAG stands for Retrieval Augmented Generation and it’s used for AI-powered tasks to retrieve precise information about specific subjects from documents or web links you provide
  • Using RAG, you can control the content your AI assistant pulls information from to give precise responses to customer queries
  • Your AI assistant can retrieve information from an unlimited number of documents and files
  • Information you send out to customers will have appropriate context for your organisation thanks to specific detail added by RAG
  • You can use RAG to generate AI-powered responses that have a depth of information you might only get from the most experienced, knowledgeable human agents in your support teams

What is RAG?

RAG stands for Retrieval Augmented Generation. It’s used to retrieve precise information about specific subjects to improve (augment) requests for information by adding details from documents or web links you provide. An LLM (Large Language Model) like GPT can use this detail to generate automated responses.

Where 82% of small business owners believe AI will “disrupt their business over the next 5 years” if not “completely transform” it, and McKinsey estimates “half of today’s work activities could be automated between 2030 and 2060,“ RAG pushes AI technology further ahead to support the assertion it can only continue to gain momentum.

How RAG assists and improves AI automation using LLMs

Basic chatbots from the 2000s were upgraded in the 2010s with the introduction of natural language processing (NLP), so advanced AI assistants of today can understand anything your customer (or anyone) asks of them. They can also integrate with all your go-to business apps to carry out tasks end-to-end for you without any support from human agents.

Now, the mainstream use of LLMs in the 2020s is transforming customer support services and generative AI more widely has captured global attention. “ChatGPT acquired 1 million users just 5 days after launching in November 2022” and by 2024 research from the content platform Writer found “97% of companies anticipate new teams, such as training, customer support, and HR, to adopt generative AI in the near future”.

AI assistants now have the power of the entire internet behind them to retrieve information they can serve up in seconds, but unless you bring LLMs safely into your business, information from them will be generic and sometimes inaccurate.

LLM-only content is unreliable

Say you want to find out about membership for a professional association. Ask a chat-oriented LLM like ChatGPT about this and it might serve up general information about membership. If the LLM can’t find an answer to the question, though, it might make one up (hallucinate). It will also deliver different responses each time it’s asked, so different visitors could get different answers.

Image of two separate chat windows showing two different responses to the same question:

To minimise the risk of giving out incorrect or inconsistent information, you need to make sure any AI platform you use has ‘guardrails’ in place to protect your organisation and a human in the loop to help train your AI assistant, so every response that goes out is accurate and relevant.

A trained AI assistant gives out accurate, consistent responses

With an advanced AI assistant platform, it’s easy to train your AI assistant to recognise specific queries and give out the exact same response every time someone asks about it. This is a far more reliable way to communicate with customers and training simply involves adding missing information and correcting responses, so they’re always right, relevant and up to date. You can update this information instantly any time.

The beauty of adding RAG capability to this set up is you can drill down further to offer more precise or detailed information that’s specific to your organisation, making sure all the right information is there every time, and on our platform, anyone on your team can take on this role of AI administrator.

Chat window showing an accurate response to the question:

RAG adds context to trained responses

Using RAG, you can control the pre-approved content your AI assistant pulls information from:

  • Retrieval = Your AI assistant can retrieve information from an unlimited number of documents and files you expressly upload for it to use to find appropriate responses to queries
  • Augmented = Information you send out to customers will have appropriate context for your organisation thanks to precise detail added by RAG
  • Generation = Information is automatically generated through an AI assistant, so human agents who once had to find this information manually now get that time back

Image of the acronym RAG: R = Retrieval A = Augmented G = Generation

Remember! With the right platform to create and manage your AI assistant, you can train it to perfect its responses, but also have it carry out tasks for you, like taking bookings or processing payments, and this extra step is vital if you want to successfully scale your service for a complete digital transformation.

On the left is a document showing membership costs and on the right, we see this information transferred into a chatbot conversation

Using all the features of AI assistants to your advantage, you can completely transform your services and operations. RAG is yet another exciting addition to your AI toolbox.

How RAG is used by enterprises today (with examples)

Embracing the power of generative AI, businesses have been using it to do more with their data at scale, but before they learned how to bring LLMs safely into their business, some leaders were left embarrassed by responses where chatbots had been trained only on public data and were plagued by LLM hallucinations.

Here are just a handful of examples of ways you can use RAG to train your AI assistant on private, company-specific data, so no business leader is left red-faced:

Example 1: Data insight for a local council

Work with unfathomable amounts of data with ease using your own approved content to transform your insights and make better business decisions. You can pull this data from across every part of your business to banish siloed or out of date information for good.

Andy Cooper, Customer Services Manager at Coventry City Council, says their AI assistant helps “capture vital intelligence to improve the services that really matter to our customers. For example, data demonstrating that parking is the most popular topic right now, helps us make the relevant changes and allocate resources more effectively”.

Example 2: Information resource for an education provider

Make your information accessible and demonstrate authority with accurate, manicured data collated at scale. For example, to create teaching materials out of thousands of syllabus documents, marking criteria, and exam papers you own to help teachers all around the world create lesson plans with trusted learning materials.

We see a list of documents on a computer screen with the title

Example 3: Compliance for a financial institution

For highly regulated industries, keeping up with risk assessment and compliance standards is a tall order but, using RAG, you can extract highly specific pieces of information from a multitude of regulatory sources, so you can meet compliance and regulatory requirements.

Example 4: Diagnostics for a healthcare company

Pull together medical records, lab reports and research papers to extract information that helps diagnose complex medical problems. By analysing previous or similar cases, you can then plan appropriate treatments and care for each patient individually with due care.

Example 5: Pinpoint part numbers for an engineering company

Upload machine manuals to find part numbers in seconds for machines that needs fixing. Find part numbers from even complex manuals or large files, no matter how many of them you have, ready to order parts quickly to limit downtime of the machine and loss of sales.

Using RAG for customer service

Arguably one of the strongest arguments for using RAG is to serve your customers with the most reliable information possible, so they can rely on your AI assistant for instant answers to all their most pressing enquiries. That’s why 83% of Legal & General customers who use their AI assistants turn to it first, rather than call in or email.

On top of all the features that make modern-day AI assistants so insanely useful, RAG is another tool that makes it easy to take full advantage of innovation in customer service to:

  • Recreate the depth of information you might only get from the most experienced, knowledgeable human agents in your customer support teams
  • Instantly reveal detailed and precise information from all across your organisation that would currently take substantial time and expertise to be able to uncover and share
  • Make sure every member of your team and every customer has the same access to your most up to date product and service information
  • Delight customers with fast, accurate, personalised answers to all their routine and repetitive queries, no matter how complex

“Using NLP, LLM and integration together results in trustworthy, reliable and easy to manage AI assistants. NLP does a great job at identifying AI requests, LLM is the AI administrator’s copilot, helping to train it and produce content from lots of different sources, and integration allows your AI assistant to do stuff, like book an appointment or access information. Where LLMs do pick up much of the leg work for you, we keep a human in the loop to prevent misinformation through hallucinations. Using reliable tech like NLP alongside LLM, and integration to add the real value, your AI assistant is then secure and scalable.”

Photo of Matthew Doel who is caucasian, wears a shirt, has short hair and glasses

Matthew Doel


Get started with RAG

It’s easy to get started with RAG when you choose the right AI assistant platform. It takes less than 10 minutes to create an AI assistant on AI Studio, which has NLP, integration, LLM and RAG built in, so you’re always using the right AI tools for the best results in automation.


What is generative AI?

Generative AI refers to AI technology that can create new content, such as text, images, or videos, based on patterns and data it’s been trained on. You can use it to generate new content, mimic human creativity, assist in content creation, provide personalised recommendations to innovate in customer service, automate tasks, and enhance user experiences to help improve your customer satisfaction score and reduce customer churn.

How does RAG help with content creation?

RAG helps you curate highly personalised and contextually rich responses to requests for information to reduce your customer effort score and improve customer engagement. By extracting insights from precise sources, RAG can help you craft content tailored to meet the unique needs of your audience, increasing overall experience and customer satisfaction scores.

When should I use fine-tuning instead of RAG?

Use fine-tuning when you want to train a pre-existing AI model on a specific dataset to improve its performance for a particular task. Choose RAG for crafting customised responses by extracting data from diverse sources and generating tailored information based on that data.

How does AI help my enterprise?

AI helps your enterprise by enhancing operational efficiency, automating tasks, improving decision-making by transforming your insights, personalising customer experiences, optimising your processes, and driving innovation across customer service, all ideally leading to an increased customer satisfaction score and retention rate.