Retrieval augmented generation: How to get more from generative AI
As more or less every professional environment continues to be reshaped by AI, staying ahead of the curve with an understanding of emerging tools and tech is critical to maintaining your competitive advantage. This goes for both individuals and organisations.
So, we’re here to keep you clued in on the latest insights, helping you leverage cutting-edge tech effectively.
Today, we’ll explore Retrieval-Augmented Generation (RAG); what it is, why it’s been making waves, and how it might be an essential addition to your tech toolkit.
What is Retrieval-Augmented Generation?
The approach of Retrieval-Augmented Generation (RAG) combines two powerful AI techniques: retrieval-based methods and generative models.
Many generative models generate text based solely on human input and pre-trained information. They can sometimes struggle to generate more specific information when it comes to uncommon or especially detailed queries.
RAG overcomes this obstacle with a key upgrade: Unlike the aforementioned models, RAG has a retrieval mechanism integrated right into the generative process.
What does that mean exactly? When the model is given a query, it doesn’t rely solely on what it ‘already knows’. Instead, it searches a database of documents in real-time (that’s the ‘retrieval’ part), identifies relevant information, and uses it to generate more accurate and contextualised responses (the ‘generation’ part).
Why is RAG Important?
While it might seem like a subtle enhancement, there are a few ways it delivers real-world results.
- Enhanced Accuracy and Relevance
Precision and context. They matter.
With a dynamic database of documents on hand, RAG models can give us up-to-date, precise responses that purely generative models can often overlook.
So for instance, if your query is in a legal, medical, or technical field where high accuracy levels can make the difference between failure and success – this is already a game changer.
Imagine a tech support chatbot enhanced by RAG. If a user submits a specific issue with a software application, the RAG model can retrieve the most recent documentation or user forums discussing that issue, meaning its advice will be current and accurate. No more wasting time trying irrelevant fixes for outdated problems!
- Overcoming Knowledge Cutoffs
Generative models have a knowledge cutoff date, (users will likely have run into this brick wall more than once) meaning they are unaware of events or information beyond their last training update. They are frozen at a moment in time.
In fast-paced industries, that’s just not good enough. Think of cybersecurity or digital marketing, where the landscape changes rapidly. Staying informed with the latest trends and threats is crucial.
RAG doesn’t have this problem; it continuously accesses the latest data from external sources. This means you can trust that your answers are based on the latest available intel. RAG is never out of date.
- Scalability and Adaptability
RAG scales effectively across domains and applications. You can make required adjustments for your industry or organisational needs, such as retrieving information only from specific databases.
Use cases could range from customer service enhancements, offering detailed product information, or support in the decision-making process.
Take e-commerce, for example. A RAG-based assistant can pull the latest customer reviews, product specifications, and stock availability quickly and easily. It’s then equipped with a comprehensive response to customer inquiries. That equals a better shopper experience, which equals boosted sales.
Preparing for a RAG-Enabled Future
So, the potential is huge. But what can your organisation do to prepare for RAG adoption, and be front of the pack when it comes to leveraging the advantages?
Here are some steps to consider:
Upskill, upskill, upskill. New technologies require new skills to implement effectively, and to apply to your business goals. Wherever you are in your AI journey, the time is now to think about upgrading the skills of your workforce to make emerging tech work for you.
In-house AI expertise will help your business to start experimenting with RAG models. For instance, testing integration into your existing workflows and systems. In turn, refining an approach in your unique business context will help maximize the benefits of this technology.
Ready to see how RAG can transform your business?