The AI frontier supercharged by RAG

As we speed up towards the end of 2024, we’re bombarded with trend conferences and tools that are designed to do all sorts of amazing things with AI. Large Language Models (LLMs), RAG, Hallucinations (is my computer seeing things? ??), Tokens, and a gazillion other buzzwords. ??

After taking a moment to catch my breath and delve deeper, I’ve actually found some great stuff out about Retrieval-Augmented Generation (RAG) and how it can drive LLMs in ways that are truly different. This might be a game-changer for your organization.

So let’s break it down, shall we? ??

• Large Language Models (LLMs): this is like having a really smart assistant who has read every book, web page and article known. So basically it′s AI, able to understand and generate natural language. These models learn the ropes of language by digesting through enormous amounts of data. Hence they can write your mails, prepare reports and even chat with you on social platforms, all with the full and delicate aroma that our words carry.
• Retrieval-Augmented Generation (RAG): RAG is where things get really interesting. RAG speeds up LLMs by looking for and grabbing relevant information. The responses of AI are then, not only intelligent, they’re spot-on. Imagine you ask a question and what you got back was tailored to your particular needs and fully up-to-date with the most recent information possible. ??

Alright, lets get real!

At Globant, we know the questions of interest for tech teams pretty well.?

Trying to wrap your head around code written by someone else can feel like trying to read hieroglyphics. It′s complex, everyone has different coding styles, legacy code written by…older and experienced developers ??, and don't even get me started on documentation. Is there any?. And don’t even get me started on the poor junior devs, hair-flying through this maelstrom of nerves, excitement, self-esteem, nothing matters except getting their first pull request done well and ASAP with almost no input from anyone.????

And here is where RAG + LLMs show up like knights in shining armor. RAG allows developers to ask questions in natural language about a specific codebase and receive answers; in this way it makes those hideous spelunking trips through mountainous piles of code a thing of much distant memory. It can guide eager devs down the right avenues; as well as providing new solutions to problems solved long ago, making all that time spent angry, 100% rewarding.

The magic behind RAG ?

When LLMs generate an answer without RAG, it's a lot like being stuck in a time capsule, as this answer is stuck with what they knew when they were trained. But with RAG, these models have immediate access to a private database of up-to-date information, not just the web but any internal documents available. This means that your models are always in the loop, dishing out responses that are not just informed but also relevant and timely. ????

In the world of software development, data is structured (neatly organized) or unstructured (like the most chaotic drawer at home that we all have). Unstructured data like emails, audio files, and code comments might appear messy but it is actually a treasure trove of insight. The difficulty is sifting it all thoroughly through, however. That’s why dev teams are embracing RAG, using it to keep LLMs up-to-date and coordinated in line with both organizational knowledge and the latest web trends.

Pro tip: Context is king. The more relevant and high-quality your input, the better your AI’s output will be.

Ready to RAG-ify your company?

Now that you have gone far enough to understand what RAG can do for your company, let’s take your organization to the next level. Now it is time to start RAG-ifying:

1. Identify where RAG can have the most impact within your organization.
2. Collect the data that your RAG model will require; internal documents, database sources, major news, etc. and make sure it is accurate and up-to-date.
3. Clean and pre-process the data in such a way that a model can easily index and recall it.
4. Choose the right pre-trained language model. Whether it be GPT from OpenAI, Gemini, CodeLlama or another model on a transformer frame--make sure that fits your needs.
5. Consider fine-tuning the model to your specific data set, in order to generate AI-generated responses which are even more contextually on point.

……and so on and on and on.

These are simply the first few steps; believe me there are a lot more where these came from. I’ve read tons of articles and even asked ChatGPT about its thoughts (and it definitely has a few!). But here’s a piece of advice: the quickest, easiest way to RAG-ify your codebases is with Augoor. We are not just keeping up with trends--we're three steps ahead, making it easier for companies like yours to make sense of their whole codebase faster and more effectively. And the best part? It's all done securely within your infrastructure--no data leaves your premises.

RAG isn’t just a buzzwordnbsp;

Rather than just being the in-word, RAG is synonymous with real business intelligence. You will be able to unlock AI's true potential in an age when information is more liquid than money and traditional tasks have been made redundant by machines.

I hope this article provided a fresh perspective on why you should keep moving towards the future with RAG. And hey, if you like reading it, subscribe to get notifications about future posts and I promise they’ll be just as intriguing and worth your time.?

Catch you later, pal! ??

Augie,

Guiding you through every line of code with AI precision