How do AI tools like large language models (LLMs) and retrieval-augmented generation (RAG) systems work together?

Firstly, LLMs are pretty smart. They've learned from tons of data and can handle all sorts of tasks, from answering questions to analyzing text. They're flexible and able to answer many prompts but are essentially limited to what they were trained on.

Sometimes, in order to provide applicable information, we need these AI models to use specific or up-to-date info that's not in their training. That's where RAG (Retrieval-Augmented Generation) systems present their use case. 

RAG systems are able to retrieve relevant data from any of the available sources and feed it to the LLM, making it better at tackling specific tasks and using current information.

By combining LLMs and RAG, we get AI that can adapt to user intent and use custom data to answer the search query.

This setup helps businesses use AI more effectively. It lets them tap into the power of LLMs while also using their own important data, which can prove extremely helpful with everything from customer service to data analysis to decision-making.

Vector Precision vs. Relevance in RAG

RAG systems use vector similarity to find the best information for a question. Vector similarity is a pretty capable method of sorting through the data and retrieving relevant information, but it's not perfect.

The issue is that even if something seems relevant doesn't mean it's accurate or up-to-date. That's a big challenge. We need to ensure the info RAG pulls up is not only on topic but also correct in every way, including its date of creation or the time frame it refers to.

Here's an example to make it clearer. Let's say you ask, "Who is the US president?" The system might find an article from 1994 that says, "Bill Clinton is the new president of the United States." The topic matches well, but the information is way out of date.

So, understanding the timing of information is very important. But that's not all. If we want everything right, we need to match the timing with other important factors.

Providing accurate answers to search queries is somewhat akin to finding the perfect puzzle piece. It needs to fit the shape (be relevant) and have the right picture (be accurate). Getting both right at the same time can be tough.

Let's look at an example. Say you ask, "What is the latest news with Customer John Doe?" You'd want to know everything important, right? All the recent stuff, any past issues, the works. However, an LLM might miss some crucial bits from earlier interactions.

It's like asking a friend for a movie recap, and they only tell you about the ending. Sure, it might be correct, but it's not the full picture.

This is a big deal for businesses, as they need to operate with a complete picture in mind, and all of the relevant information must not be left out.

Aligning Answers with User Intent

When we talk about the answers that AI gives us, we primarily focus on how well they align with the user intent. We call this content relevancy.

When you ask a question, you want an answer that fits just right. Not too broad, not too narrow. That's what we're aiming for with AI.

To get that perfect answer, the AI often has to piece together bits of info from all over the place. Some bits might be spot-on, others might be only sort of related.

So, how do we figure out which pieces are the most useful? That's the big question. We need to be able to measure how relevant each bit is, toss out the stuff that doesn't fit, and decide how important each piece is for the final answer.

Let's look at an example. Say you ask about recent AI breakthroughs. The system might pull info from new research papers, news articles, and even some older background stuff. To give you a great answer, it needs to figure out which of these sources are most relevant and how to mix them together to provide an overview that satisfies the user intent in a way that is just right.

The Need for Up-to-Date Information

Another factor that determines how valuable and applicable the search results are is time relevancy.

Time relevancy is about making sure AI answers are both accurate and up-to-date. Often, the newest info is most important. Old answers can lead to mistakes and make people lose trust in search engines.

Take cybersecurity advice for example. Information in this field needs to be current to deal with new threats. Old methods might not work anymore. 

On the other hand, some info, like what a DNS server is, doesn't change much. So AI needs to balance being current with being accurate, depending on the question.

For AI to be time-aware, it needs to understand when timing matters in questions and data. If you ask about the latest AI trends, it should look at new articles first. To do this, it has to figure out the date of each piece of info, ignore the irrelevant stuff, and rank what's left by how recent it is.

But making this work isn't easy. Questions often don't state the time frame they're referring to. "Last year" or "2020" are clear, but many questions aren't.

It's also tricky to distinguish between when the info is really from and and the time the info is referring to. A recent article about the 1990s greatest hits is about the 90s, though written nowadays.

Sorting out these time issues helps AI give correct and relevant answers.

Time Filtering & Handling in RAG Systems

It's essential that the information used is up-to-date to provide accurate answers. A trained language model can classify a question's time frame. This classification can be accomplished by tasking a trained LLM with classification, providing it with supported instructions, examples, and edge cases. 

The question's time category is then used to select relevant sources from the RAG database.

Implementing time-based filtering and sorting involves strategically managing data sources based on their temporal context to ensure relevance. This process starts by accurately identifying the time or time period referenced by each data source, whether it's the publication date or the historical context covered. 

If conflicting information is found, we can weigh the sources by their time relevance. This involves favouring more recent sources for current topics or appropriately weighing historical data when assessing past events.

Conclusion

By understanding when questions are asking “about and when” of available data, we can make AI systems even more useful, especially when time is important.

As AI gets smarter, staying up-to-date will be even more crucial. We can expect to see better ways to filter information by time, use real-time data, and understand the time aspects of questions, and if we keep constantly improving these systems, we'll ensure AI remains a powerful tool for making informed decisions, providing insights that are not just right, but also current and applicable.

‍