We’ve all been there. You read about something the other day, but you aren’t sure anymore where it is stored exactly. You try endless keyword searches, but still your computer says no. If this sounds familiar to you (for 99% of knowledge workers it does), then definitely RAGs are something for you to implement. Let’s explore how you can make the above-mentioned drag into a rag so you find the needle in the haystack!
What is Retrieval-Augmented Generation (RAG)?
Basically, a RAG allows you to get ChatGPT-like responses on your (internal) knowledge base with high contextualize answers. In short, as a user, it is something like an AI-powered Google Search for your company’s information.
Why RAGs are so disruptive to knowledge workers:
Let’s say you’re a financial analyst, a lawyer or a research consultant needing to sift through hundreds of documents to find key details. A RAG system can search through all the relevant texts and generate a concise summary of the most important points, saving you hours of time and effort. It’s somewhat like having a very smart intern or a junior researcher at your disposal who does the searching & summarizing for you. For a list of use cases, check here.
How it works:
Here’s how it works in simple terms:
The Retriever: This part of the system searches through a large collection of documents, databases, or even the internet to find the most relevant pieces of information related to a specific query.
The Generator: Once the retriever has pulled in the best information, the generator (a model like GPT) takes that data and formulates a response, ensuring it’s coherent and fits the context of the conversation or task.
By combining these two steps, RAG can provide more accurate, relevant, and up-to-date answers compared to models that rely solely on pre-trained data.
Schematically it looks like this:
What about data privacy & GDPR?
Many of the same rules apply for data privacy & GDPR as to any other software technology. However, a tricky thing with RAGs is that data that is often now being available for search with AI wasn’t anonymized or pseudonymized as you typically see in databases used for software.
This has everything to do with the sources of data like e.g. (employee or customer) contracts that have been saved in .pdf or .docx and now become available through search.
Some principles are recommended depending on the architecture you prefer:
- Anonymization and Pseudonymization: When possible, the data used by RAG models should be anonymized or pseudonymized to reduce the risk of data breaches.
- Limit Data Retrieval: Only retrieve and use data that is absolutely necessary for the task at hand. RAG models should be designed to avoid over-collection of personal data.
- Privacy-Preserving Retrieval: Use retrieval mechanisms that prioritize privacy, ensuring that personal data is not included in the retrieval corpus unless necessary and properly justified.
Besides the architecture, it is important to host both the LLM that generates as the datasource where the retrieval happens is hosted in the EU. It is also highly recommendated that the LLM used is not using your data to train the model further.
Wrapping it up
In a nutshell, Retrieval-Augmented Generation (RAG) combines the best of two worlds: fast and accurate information retrieval with the fluency of AI language generation. This combination makes it a perfect fit for everything from financial analysis, legal research work or even due diligence. RAG allows AI to be smarter, more relevant, and more responsive, pushing the boundaries of what AI can do in fields ranging from healthcare to news and content creation. As AI continues to evolve, RAG will likely become an integral part of how we interact with and leverage technology in our daily lives. Search will no longer be a drag, but a RAG!
Want to learn more?
Are you interested in how your organisation can benefit from Retrieval Augmented Generation (RAG) to give superpowers to your knowledge workers? Book here a demo to learn more about Structize AI!
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