2024 is set to be a revolutionary year for artificial intelligence technology, thanks to something called 'Retrieval Augmented Generation', for friends RAG. To understand what this means, think about models like ChatGPT, which we know for their dialogue capabilities. RAG takes these programs to a higher level, allowing them to 'draw' information from external sources, almost as if they could consult an encyclopedia or database in real time to provide more precise and detailed answers.
What does it mean? It means that when we talk to these systems, we not only receive answers based on their 'digital brain', but also accurate and up-to-date information from around the world. And it will bring yet another revolution in the sector. Rag retrieval augmented generation, gentlemen. Memorize the name.
The impact of RAG on companies and people
The advent of RAG has profound implications for both businesses and consumers. For companies, means being able to integrate advanced language models with your own databases and specific knowledge, leading to unprecedented efficiency and effectiveness in customer interactions and process automation. For consumers, translates into richer and more personalized digital experiences, thanks to chatbots and virtual assistants capable of providing relevant and updated information in real time.
In 2024, 3 fundamental "standard bearers" will emerge:
1. Codeless systems
AI systems that will not require coding skills and will use RAG will boom. Consumer versions of ChatGPT are set to be among the most sought-after in 2024. These systems will enable even people without technical skills to build complex generative AI capabilities, breaking down barriers to entry and democratizing access to advanced AI technology.
2. API RAG
RAG APIs, such as those offered by OpenAI, offer businesses the ability to create generative and sophisticated AI chatbot capabilities, using user- or website-specific data. This will greatly simplify the development of AI applications, making these projects accessible to an even wider audience.
3. RAG Workflows
Cloud platforms like Salesforce and Zoho are already integrating workflows based on RAG APIs, making it easier to access account-level data and create new, highly efficient workflows. This opens up new possibilities for dynamically generating AI content in a variety of applications, from generating PDF documents to personalizing user experiences.
Practical examples of use
1. Personalized customer support
Let's imagine an insurance company implementing a RAG-based chatbot. A customer can ask about the coverage of his policy in case of damage caused by natural events. The chatbot, using RAG, not only understands the question, but also draws on specific information from the customer's policy and recent data on natural events in their area, providing a personalized and detailed response.
2. Real-time decision support for businesses
Consider a company that needs to make rapid data-driven marketing decisions. A RAG system can analyze massive amounts of sales data, customer feedback and market trends in real time, providing recommendations based on the latest and most relevant data, helping managers make informed and timely decisions.
3. Personalized individual teaching
A RAG-based educational application could provide students with personalized study assistance. For example, a student studying history might ask specifics about the causes of the First World War. The RAG system can draw on up-to-date and accurate historical sources to provide a detailed explanation, enhanced by historical data and expert analysis, and present it to the student in the language best suited to them.
4. Health care and medical advice
In healthcare, RAG can be used to provide personalized medical advice. A user could describe their symptoms to an application which, using RAG, consults medical databases and clinical studies to provide possible diagnoses or advise when it is necessary to consult a doctor, considering the user's medical history and the latest medical research carried out. Goodbye agonizing improvised searches on medical sites.
5. Planning of trips and itineraries with management of unexpected events
An online travel agency could use RAG to offer customers personalized travel itineraries. Based on customer preferences, current weather conditions and local events, the RAG system could suggest destinations, activities and travel recommendations that exactly match the customer's expectations and interests. Even trying to change stages "on the fly" to adapt to sudden changes in budget or situation.
6. Advanced financial analysis and reporting
In the financial industry, RAG can be used to generate custom analyzes and reports. An investor might ask for an analysis of current stock market trends. Using RAG, the system could draw on real-time market data, financial reports and expert analysis to provide an up-to-date and in-depth overview, helping the investor to make informed investment decisions (and somehow predict the performance of markets ).
7. Automation of business workflows
RAG can also be used to automate and optimize business workflows. As? For example, in a manufacturing company a RAG system could monitor supply chain data in real time, predict potential problems and suggest corrective actions, thus optimizing production and reducing downtime.
Mine are just some of the examples that illustrate how Retrieval Augmented Generation can transform different sectors, offering personalized solutions, accurate information and decisions based on real-time data.
In summary: 2024 will be the year of the RAG
Enthusiasm for advanced language models was high in 2023, but we were only seeing dress rehearsals. 2024 promises to be the year when practical applications and end-user benefits will increase exponentially. RAG will not only change the way we interact with AI, but will also open up new horizons of possibilities, both in business and personal settings.
The promise of RAG is to take AI into uncharted territory, where not only the ability to intelligently generate text is critical, but also the ability to inform and adapt based on an ever-evolving universe of data. 2024 will be remembered as the year artificial intelligence truly began to understand the world around us.
