The adoption of generative artificial intelligence has entered a new phase. Its business implementation on a large scale is underway, and many entrepreneurs who previously overlooked its potential are now seriously considering investing in genAI. Your company might also be thinking about such an implementation, but could have concerns about its efficiency and accuracy. Luckily, solutions like RAG can ensure LLM models provide high-quality responses, maximizing GenAI benefits seamlessly.
RAG (Retrieval-Augmented Generation) is a framework that every company implementing genAI should consider, whether as a “corporate superbrain,” a virtual assistant, or for process automation. Before we explain how to implement it effectively (we do this every day), let’s take a closer look at RAG and try to understand its role in a successful implementation.
What is RAG (Retrieval-Augmented Generation)?
RAG, or Retrieval-Augmented Generation, is a technology that incorporates a hybrid approach to content generation. It retrieves relevant information from a large database and uses that information to generate precise and contextually accurate responses. In other words, instead of relying just on the training data sources, it references the authority knowledge base to generate a response, making it more reliable and informative.
How does RAG work?
Imagine you’re throwing a last-minute dinner party, and you need a recipe for a fancy dish, but your cookbook collection is overwhelming. Instead of flipping through every page, you ask your AI assistant, “What can I make with chicken and asparagus?” The assistant not only pulls a delicious recipe from your cookbook database. It also adds some clever cooking tips and a wine pairing suggestion, making you shine as a dinner host. Thanks to RAG, you get a spot-on answer without any hassle.
In the same way that RAG could make you look like a culinary genius, it could also provide better answers to your company’s employees and customers. And byecuring its reputation and efficiency. Every time the large language model is supposed to generate an answer – whether it is the customer asking about the product’s specifics or an employee asking about company policy – it confronts its state of knowledge with the sources you provided it with.
How does RAG in AI tackle the issue of LMM’s unpredictability?
LLMs are inherently unpredictable due to the nature of their training and context sensitivity. Their learning trajectory depends on the order and composition of the small data batches the model is sampled. The response they provide is context-based. That means its quality strongly depends on the information provided in the prompt. Length, complexity, style, straightforwardness – all these aspects impact the response, even if they vary slightly.
The complexity of the human language does not help with predictability either, forcing the model to choose an interpretation that is not always the right one. Note that the LLMs learn through feedback, asking users which response serves them better. The users, analogically, learn how to formulate their prompts to be understood better. Despite these mechanisms, interpretation is an issue that impacts the success of genAI implementation.
All this adds to LLM’s unpredictability, a major downside for customers using generative artificial intelligence. Business owners need genAI tools to provide reliable answers. Accuracy and quality directly affect customer satisfaction, efficiency, and sales. Using RAG can achieve this reliability.
Let’s put it this way: if the model recommends the wrong film, the worst outcome is everyone falling asleep. However, incorrect information about company policy or non-compliant images have severe consequences. Hence, incorporating RAG is crucial in GenAI implementation.
Why is RAG good for your business?
Now, let’s talk business – how can successful genAI implementation with RAG make your company thrive? Speaking briefly, it will enhance:
- Accuracy and reliability: RAG ensures that AI-generated content is based on verified data, reducing the risk of misinformation.
- Efficiency: It speeds up the information retrieval process, saving time and resources.
- Customer experience: By providing precise and relevant information quickly, RAG improves customer satisfaction and engagement.
- Scalability: RAG can handle vast amounts of data and provide consistent, high-quality responses, supporting business growth.
- Savings: By automating information retrieval and content generation, RAG reduces the need for extensive manual research and customer support interventions.
- Your reputation: RAG provides verified answers rooted in your verified data sources. That helps avoid errors that could increase churn or even cause legal issues.
RAG use cases. How Retrieval Augmented Generation can provide value to your business?
Let’s look at RAG implementation through the prism of three different sectors to illustrate its potential benefits.
E-commerce: Resolving Order-related Issues
Imagine you are running an e-commerce store, and your customer, Jane, is worried because her package is late. Instead of wading through a maze of FAQs and support tickets, she simply asks your GenAI virtual assistant, “Where’s my order?”
The assistant doesn’t just pull up the tracking information. It dives into your shipping databases, customer order history, and even current weather conditions affecting deliveries. It then gives Jane a detailed update on her package’s location, as well as expected arrival time, and offers a discount on her next purchase as a goodwill gesture.
Thanks to a custom AI system, Jane gets a precise, empathetic answer without any hassle. Result: she is impressed with your customer service! With RAG, you can be sure that the way your AI assistant adapts its autonomous actions will be precise and correct.
Venture Capital: Finding Startups with Potential
Imagine you are a venture capitalist seeking your next big investment. You ask, “Show me promising startups in the biotech sector.” Your GenAI assistant filters through the noise, highlighting startups based on funding trends, patent filings, and market sentiment.
It even adds fun facts about the founders’ backgrounds and recent achievements, making decision-making smarter and more engaging. You can choose data sources, including credible financial databases from verified providers. With RAG, you discover hidden gems traditional methods might miss, giving your company a competitive edge.
Drug Development: Finding New Potential Drug Recipes
Picture this: you’re a researcher in a pharmaceutical company, tasked with finding new drug recipes. Your GenAI assistant, enhanced with RAG, helps scan through research papers, clinical trial results, and chemical databases.
Suppose you need compounds to replace a current drug causing side effects. With RAG, your GenAI assistant extracts useful knowledge from the latest studies, historical research, and experimental treatments, significantly cutting research time. It then structures the information, providing detailed explanations of mechanisms and potential efficacy.
How to Include RAG in Your GenAI Implementation
To implement a RAG system, start by gathering comprehensive data relevant to the process you want to streamline with GenAI. It could be, for instance, product data, including descriptions, ingredients, usage instructions, and reviews. Additionally, collect customer data, such as purchase history, preferences, and behaviors. Prepare this data by organizing it in a searchable format. Clean it to eliminate inconsistencies and errors.
Step 2: Choose a Suitable RAG Framework
Next, choose a suitable RAG framework that supports both retrieval and generation capabilities. Develop the retrieval component by implementing a search engine that can efficiently index. Retrieve product information based on customer queries and optimize the search algorithms to ensure relevance.
LangChain and LlamaIndex are two most popular libraries at the moment. LangChain is very popular due to its versatility, user-friendliness and compatibility with a range of LLM models. However, if speedy retrieval and the capacity to process large chunks of data is your priority, go for LlamaIndex.
Step 3: Integrate the Generation Component
Once it’s done, integrate this with the generation component by setting up a pre-trained language model (like GPT-4) to generate natural language responses. Combine the retrieval and generation systems to fetch relevant data and produce accurate, contextually appropriate answers.
Step 4: Train and Fine-Tune the Model
Finally, train and fine-tune the model using historical customer queries and responses to improve its understanding and accuracy. Combining RAG and fine-tuning can make the results provided by your LLM even better. Here, we explain how you can combine these both in an RAFT framework and ensure success!
Need Help?
Sounds complicated? That’s what we are here for as a generative AI services company. Reach out to us and combine the powers of RAG and fine-tuning in your custom GenAI implementation!
