There’s no doubt that generative AI is an excellent and exciting business tool filled with potential. But getting it to work for your business, with all the nuances and complexities specific to your work, is complicated.
After all, one of the most exciting things about the emergence of generative AI is how generalist it is, capable of working impressively in lots and lots of different contexts. The trade-off is that generative AI often isn’t fantastic at specific tasks and in particular contexts. It is limited by its training data.
If you want generative AI to really work for your business, you have two options. Fine-tuning a large language model or deploying a retrieval-augmented generation (RAG) system.
To make sure you’re up to scratch on everything this tech can do, check out this guide to generative AI. Then read on to explore the pros and cons of a RAG solution.
Pro 1. Accurate answers on specific and confidential information
One of the best things about RAG is it can securely handle domain-specific and confidential information, because it keeps it separate from the LLMs training data.
That means it can refer to proprietary information about your business, or personal information about your users to accurately and quickly answer specific questions.
Pro 2. Transcends the limitations of training data
Another strength of RAG solutions is the ability to access real-time or up-to-date data.
LLMs have a huge knowledge base, but it is cut off at a certain date, limited to public information, and has other shortcomings as outlined in this review of LLMs’ limitations.
So, you can’t ask a generative AI built without RAG about the price of gold this morning, or which shipping routes will be least affected by the weather this week.
RAG allows you to bring the immense analytic power of generative AI to bear on the very latest information.
Pro 3. Can provide real-time guidance
Perhaps one of the most appealing possibilities unlocked by RAG solutions is the potential for real-time assistance.
RAG systems can take the user’s live actions as a supplementary input. That means that as the user writes, or codes, or practices a language, the generative AI system can keep track of what they’ve tried before and what mistakes they keep making.
You can see how code assistants input and output between RAG, LLMs, and the user in this article’s illustration of a RAG setup.
Con 1. Adds complexity to your application
As great as all this sounds, it’s not easy. Adding in a RAG system means you need to manage both the generation model and the retriever component.
Not only does your generative AI application need to produce an answer that makes sense, but it also needs to check the RAG database for context specific information that will make its answer as accurate and up-to-date as possible.
Con 2. Adds extra ethical considerations
The more of a user’s data your generative AI application uses, the more there is you need to make your users aware of. This can’t be understated – check out this excellent deep-dive into the many ethical considerations of machine learning projects.
Personalization is great, and most users will jump at the chance to have a system that responds to their specific actions.
But to stay on the right side of current regulation, you need to make sure your users are aware of what information your application might gather about them, what it might do with it, and give them the option to opt-out or request data erasure.
Con 3. Limited creativity compared to fine-tuning
The retrieval information available to an application using a RAG system will make it great at answering certain questions, but it can also make the application less creative and more restricted to the retrieval data.
Fine-tuning, by contrast, is inherently more flexible and creative as it allows the application to learn more about the task as it goes, rather than simply retrieving specific data.
Ultimately it comes down to what the aims of your application are, but if you’ve found this helpful, check out our other tech guides.
Final Words:
In conclusion, generative AI offers immense potential for businesses, but its implementation requires careful consideration. While its generalist capabilities are impressive, specialized tasks may need fine-tuning or retrieval-augmented generation (RAG) systems for optimal performance.
RAG systems excel in handling specific and real-time data but add complexity and ethical considerations. Fine-tuning offers flexibility and creativity but may lack precision in certain contexts. Choosing the right approach depends on your business needs and goals.
By understanding these options and staying informed, you can leverage generative AI to drive innovation and efficiency in your business. For more insights, explore our other tech resources.
Tags: Retrieval-Augmented Generation paper, Retrieval Augmented generation medium, Retrieval Augmented generation LangChain, Retrieval augmented generation example, Retrieval Augmented Generation HuggingFace.