Reach the full potential of your data

Generative artificial intelligence is on the rise, and frequently, in my

meetings with executives from large companies, two questions arise with certain

frequency:

“How can we use generative AI without suffering from the unpredictability of responses

these models?” and “How is it possible to integrate generative AI into the dataset and

specific knowledge already existing in our organization?”

Faced with these issues, a technique has been gaining prominence: Retrieval-Augmented

Generation (RAG).

Making RAG uncomplicated

RAG enhances LLMs by integrating up-to-date, company-specific information

organization to the response generation process. This results in virtual assistants

who not only understand general issues but are also experts in the

particular context of the company, offering precise and personalized solutions.

A simple example that illustrates the application of this technique well is in the

automated customer service. Imagine you are talking to a chatbot from a

seat equipped with RAG. Instead of just giving you basic information about

loans, as a conventional chatbot would do, this chatbot can consult

directly the bank's credit rules and your financial data.

This way, he can give you very specific answers. For example, if you ask about the possibility of getting a loan, the chatbot analyzes your financial history and current bank regulations to offer you details such as the interest rates you would pay and suggestions tailored to you.

Practical Applications of RAG

I have listed below some examples of real solutions that can take advantage of

capabilities of LLMs thanks to RAG:

1. Personalized Customer Service : By integrating RAG, chatbots can access and apply information specific to company policies and customer data, dramatically increasing the quality of service.

2. Intelligent Knowledge Management : RAG facilitates access to relevant information stored in the organization's data repositories, making it possible to create different solutions that make employees' work more efficient and productive.

3. Advanced Data Analytics : In industries that rely on rapid, data-driven decisions, RAG enables rapid, in-depth analysis of large volumes of data, providing critical real-time operational and strategic insights.

4. Personalized Legal Support : You can use RAG to create intelligent assistants that offer tailored legal advice based on company-specific legal precedents and regulations.

5. Supply Chain Optimization : RAG is applied to predict problems, optimize logistics and reduce costs in complex supply chains by dynamically adapting to market and demand changes.

6. Compliance and Risk Monitoring : RAG can also be crucial for solutions that monitor compliance with industry regulations, alerting you to potential breaches that could affect critical areas such as finance and healthcare.

Strategic Partnership with Databricks:

Databricks has a complete suite of tools designed to improve

implementation of AI applications, including Retrieval-Augmented Generation (RAG).

TreeID is an official Databricks partner and thanks to that I had the opportunity to test the

platform. Speaking specifically about RAG, I would highlight:

• Real-Time Access to Data : Databricks provides solutions that enable integration and immediate access to up-to-date data, essential for accurate and personalized responses in AI applications, such as advanced virtual assistants.

• Model Selection and Optimization : The platform offers features that greatly facilitate the choice and use of language models, integrating natively with the most used models in the corporate environment, such as Azure OpenAI, AWS Bedrock and Anthropic, with open source models such as Llama 2 and MPT, but also allows integration into fully customized models adjusted according to the customer's needs.

• Quality and Security Assurance : With Databricks Lakehouse Monitoring, taking care of the quality and security of RAG applications becomes much simpler and more direct. This tool automatically checks application responses for any problematic content, such as toxic or unsafe information. And through very intuitive dashboards, it is possible to see in real time how the application is performing with detailed metrics, such as the acceptance rate of recommendations made by AI. This makes it much easier to make quick adjustments, guaranteeing the planned results of the application and ensuring compliance with corporate security and privacy standards.

The integration of these tools solves the most common technical challenges, ensuring

that RAG applications are developed more quickly and ensuring that

are accurate, current and aligned with the specific business context.

Conclusion

Thanks to techniques like RAG, the use of generative AIs tends to increase more and more

more at the corporate level, providing infinite possibilities to maximize

operational efficiency with the precision and safety that this type of environment requires. AND

platforms like Databricks certainly accelerate this process, while

which guarantee all the necessary governance for this type of application.