Generative AI and RAG
Generative AI and RAG (Retrieval-Augmented Generation)
Generative AI is a technology that uses advanced machine learning algorithms to create new content, such as text, images or audio, from existing data. It has been widely applied in various areas due to its ability to generate high-quality responses and even mimic language patterns or communication style, according to the need and context of each application. A modern approach in the field of generative AI is RAG (Retrieval-Augmented Generation). RAG is an architecture that combines content generation with information retrieval. Rather than relying solely on a trained AI model, RAG searches for relevant information in external sources, such as databases, documents or the web, and uses this information to generate more accurate and contextually relevant answers. The AI model then synthesizes this information and presents it in a cohesive way, extending the reach and effectiveness of the answers generated in the corporate environment.
Benefits of RAG
1. more accurate answers: RAG improves the accuracy of responses, as it allows AI to consult external sources to obtain up-to-date and specific data.
2. Reduced computing costs: By seeking information from external sources, RAG can reduce the need for training on large volumes of data, optimizing computing resources.
3. Contextualized response: The ability to access documents and detailed information allows AI to produce more contextual responses that are targeted to the needs of each company.
Generative AI and RAG Use Cases
1. Increased Productivity in Searching for Information in Internal Documents
In the corporate environment, generative AI, in conjunction with RAG, can speed up the search for and retrieval of information in large volumes of internal documents. Instead of manually searching through extensive files or databases, employees can ask questions of the AI, which automatically consults the relevant documents and generates quick, accurate answers. This significantly increases productivity and reduces the time spent on research tasks. MPL has already implemented RAG projects in several companies. If you would like to try it out, please contact us.
2. Improving the Customer Experience
Generative AI with RAG can transform the way companies interact with their customers. Instead of navigating through lengthy manuals or tutorials, customers can ask questions directly to the AI, which responds clearly and concisely, based on the most relevant information. This not only improves the user experience, but also speeds up the process of resolving queries and problems.
3. Financial Market - Clarification of Policies and Contracts
In the financial sector, generative AI with RAG can be used to clarify doubts about policies and contracts quickly and accurately. Customers can ask about specific terms or conditions of their contracts, and AI can search through the documents to provide a clear explanation. This not only improves the customer experience, but also reduces the workload of human support, allowing agents to focus on more complex cases.
4. Retail - Technical Support without the need for lengthy manuals
In the retail sector, generative AI can be applied to answer technical questions about products without the customer having to download and read lengthy manuals. For example, when looking for information about the functionalities of an electronic product, AI can provide a detailed explanation based on the product's manuals and specifications, improving service efficiency and customer satisfaction.
5. Increased efficiency of human agents in Customer Service
Generative AI and RAG can optimize the agent's service to the customer by summarizing requests, quickly consulting historical information, in regulations and procedures, providing accurate information. This allows service agents to focus on the human aspect of the interaction, while AI takes care of information searches and direct responses, speeding up the service process and improving service efficiency.
In short, generative AI and RAG represent a powerful combination for improving productivity and the customer experience, while streamlining internal processes and providing more precise and contextually appropriate responses.
These technologies are shaping the future of customer service, the financial sector, retail and many others, creating new opportunities for innovation and efficiency.
MPL has won international awards for its Artificial Intelligence innovation projects.
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