Key Takeaways

• RAG enhances LLMs using an external knowledge source, giving customers more accurate answers.
• RAG-backed LLMs don't need retraining. You can add the updated information to your knowledge base, and the AI will scrape only the most updated information.
• RAG has extensive applications for customer service teams. These include fully automated Tier 1 support chatbots, augmented responses for human agents, and multilingual support.

Retrieval Augmented Generation (RAG) is an artificial intelligence framework that makes large language models (LLMs) more powerful by incorporating external knowledge sources. For most businesses, this external data includes your company's product demos, catalogs, and blog.

AI professionals train LLMs on vast amounts of publicly accessible online data. But without help, an LLM can't access the knowledge contained in company-held data warehouses.

By granting RAG access to those warehouses, you augment the LLM. Your AI-generated responses to customer or employee queries will be domain-specific and accurate.

RAG has numerous applications for your business, from finance to customer service. Keep reading to learn about these applications and why RAG is worth the investment.

How Does RAG Work?

RAG combines two essential components: external sources and information retrieval.

External sources are the knowledge bases created by your business.

Information retrieval is the mechanism that scrapes these external sources and cross-references them with the user's query to help generate more accurate, relevant, and contextually appropriate responses.

Sure, LLMs also function by scraping vast amounts of information for an answer. But RAGs operate a bit differently.

The difference stems from RAG's process to get from prompt to response:

1. The user enters a prompt, triggering the data retrieval system.
2. The RAG retrieval model ingests two data sources. The first is your structured data, which includes business and customer information stored in tables and spreadsheets and housed in a data management platform. Unstructured data is the second source, which includes blog posts and support documentation.
3. The RAG system converts the data into embeddings. These embeddings help the retrieval model understand the context and nuances of words and phrases. The embedding model recognizes if the user's prompt is specific to your company and not general.
4. The retrieval model then crafts an enriched prompt to feed the LLM with these embeddings. This process helps the LLM generate an accurate and relevant response to the user query.

By themselves, most LLMs can only use static training data to provide answers. By using external sources like your customer data, RAG enables an LLM to provide more accurate responses. Your business can have multiple role-based RAGs that pull from separate knowledge bases.

For example, a customer-facing, RAG-powered chatbot shouldn't be able to access sensitive company financial data. But that data might be essential for a member of your accounting team so you could train a separate RAG for internal use.

Why Use RAG?

RAGs enhance LLMs in five key ways:

#1: Reduce Hallucinations

AI hallucinations are inaccurate responses. They occur when the LLMs public training data is out-of-date or insufficient. LLMs can't determine whether their training data is reliable. They simply generate the best possible response to a query based on the data they have.

Hallucinations can damage a company's reputation and even lead to financial losses. Take Air Canada, whom courts ordered to pay a partial refund to a customer after its AI provided false information about its bereavement policy. 

Retraining LLMs is a costly and time-consuming undertaking. There's also no guarantee the retraining will generate significantly better results.

RAG reduces hallucinations because it grounds LLM responses in company or domain-specific knowledge.

#2: Say "I Don't Know"

LLMs generate responses from a finite information base. If the relevant documents in that information base don't contain enough data to answer a user question, the LLM can't make sense of it, and an inaccurate answer often follows. The only way to help the LLM make sense of the question is to retrain it. RAG helps you avoid this situation by drawing on external sources to understand and contextualize user queries. If those external sources aren't sufficient to help it understand a query, much less provide a full answer, it will respond with "I don't know."

#3: Overcomes Limits of LLM Tokenization

Tokenization is the foundation of Natural Language Processing (NLP). Generative AI models can't function without it. LLMs understand their text-based inputs by breaking that text down into much smaller chunks and assigning these chunks unique IDs called "tokens." But here's the challenge. LLMs like GPT can only process so many tokens at a time due to token limits. These limits mean the LLM can't understand every possible query or respond with exacting precision.If you have a blog with hundreds of posts, a traditional LLM may pick up some textual nuances, but not enough to answer complex queries with high accuracy. RAG systems also chunk and store data, but they add "embeddings" to the mix. Embeddings connect relevant chunks of content, much like your brain recognizes semantic links between new data and data it has already.

Thanks to embeddings, RAG will only search and return the embeddings for content relevant to the user prompt.

#4: Identify Gaps in User Knowledge and Company Resources

A significant benefit of RAG systems is their ability to tell a user when they don't know something. They can also identify the most commonly asked questions they can't answer.

Now you can create new content that will answer questions. You've also added new knowledge to the RAG's knowledge base.

This process is known as "knowledge base customization." It's one of the easiest ways to help RAG provide accurate answers.

#5: No Training or Costly Upkeep

Retraining an LLM often involves a significant investment in personnel, which has a steep cost. Maintaining an RAG system is much less expensive and time-consuming. If an RAG can't respond to a query, it lets you know. The solution might be adding a single document to its knowledge base. You won't need to retrain it.Every company is different, but it might be possible for a single employee to manage the RAG while fulfilling other duties. That's much cheaper than employing a dedicated team.

A Sample of RAG Applications

Companies are discovering potentially unlimited applications for RAG, which is already in use across more than a dozen industries, including healthcare, finance, marketing, and sales.

With RAG companies can turn manuals, videos, and articles into knowledge bases, improving the user experience and saving your employees time.

Here are just a few RAG applications:

Tier 1 Fully Automated Support Chatbot

Tier 1 support agents are the first point of contact for customers who call your company for help. These agents either resolve simpler issues or transfer the customer to a more specialized Tier 2 agent. Whatever their tier, customer service agents are expensive to hire, train, and retain. RAG-powered chatbots free onshore and offshore agents to solve more complex problems, saving you money and saving your customers time. Customers no longer have to wait on hold since RAG bots can either solve issues themselves or escalate them to Tier 2 agents. Say the RAG bot escalates an inquiry to a Tier 2 agent. The agent's workflow would improve if they could quickly access the customer's support history. If the customer has reached out multiple times about the same problem, the last thing the customer wants to do is rehash the situation.An agent-facing RAG with access to the customer's account can quickly summarize details or respond to more specific agent queries.

Improved Decision-Making

Good decision-making is critical in any industry. It's especially critical in fields like healthcare where lives are often at stake.Suppose a doctor needs help diagnosing a patient's illness. The first step can be allowing RAG to compare the patient's data with a medical database of symptoms and conditions. Depending on the level of detail in the query, the RAG can help the doctor narrow treatment options, and even suggest tests for a more conclusive diagnosis.In the finance industry, RAG can layer your company's specific advisory materials on top of real-time market datasets and the latest regulatory changes. Now your clients get investment advice that is more specific to their unique goals.

Research and Analysis

RAG speeds up legal teams' research for upcoming cases. By creating a knowledge base legal briefs specific to your firm, you can query them for insights on new cases. The RAG's response would help lawyers prepare arguments and suggest ideas for further research.Suppose you head research and development at a pharmaceutical company. RAG can mine past drug studies to see if an unused chemical compound applies to a drug in development.

Content Creation

RAG can facilitate content creation for marketing teams if you integrate it with your company's blog library.No longer must writers spend hours re-reading old posts for ideas. With RAG-powered chatbots, writers can now submit queries like these:

• Has [topic X] been covered?
  • If not, then that topic can become an article.
• Which existing articles relate to [topic X]?
  • The RAG response can suggest internal links.
• Have I used [X example] in a previous article?
  • Now the writer can avoid duplication and keep content fresh.

Curriculum Design

Students and new hires learn at different paces. RAG can design personalized training and curricula based on an individual's understanding of an online course or module.Most online training modules end with a quiz. If a student or employee gets certain questions wrong, RAG can present an appropriate follow-up tutorial directly addressing those misunderstandings.

Policy Analysis

The RAG model can evaluate and compare policy options and find the most effective solutions. Suppose multiple city planners have different opinions on how to implement a new land use policy. Each planner can document their opinion and add it to the RAG knowledge base. The RAG can then evaluate each opinion against data repositories containing historic policies. Its response might suggest the best path forward. It can also create cost-benefit analyses and continuously evaluate existing policies.

Report Generation

RAG makes report generation easier by rapidly mining vast amounts of relevant information from multiple sources to support a user query. If the data is clean and easily accessible by the RAG system, there's also less risk of error in the finished report.A company CFO can ask a RAG chatbot to extract relevant data from its financial statements and create an analysis of profitability and revenue trends.

Create Your First RAG Collection with Scout

Whatever your industry, RAG makes it easier for customers and employees to find accurate answers to their questions at scale. Better answers lead to more informed purchasing decisions, more personalized support, and higher customer satisfaction.

If you're looking to optimize your AI systems with RAG, Scout is the partner you need. You'll build new AI workflows in record time.

Create your first RAG collection with Scout today, and see how it can help your business scale decision-making and customer service faster than your competitors.

FAQs:

• What about data privacy and protection when you share documents with RAG systems?
  • Prompts, responses, and knowledge bases have the potential to contain sensitive information. However, there are ways to mitigate the risks, including role-based access controls, masking direct identifiers, and detecting unauthorized prompts.
• How reliable is RAG currently?
  • We're still evaluating the accuracy of RAG systems. However, most users have discovered that the level of accuracy depends on the quality of the RAG system and its retrieval capabilities.
• What are some applications on the market that successfully utilize RAG?
  • Statsig uses RAG to help customers mine its extensive knowledge base from an easily accessible chatbot.