Retrieval-Augmented Generation (RAG) is a groundbreaking technology that combines information retrieval with text generation. Discover how RAG is transforming various industries, from HVAC and Health and Beauty to DIY and construction industry, enhancing efficiency, accuracy, and user trust in AI systems.
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What is Retrieval-Augmented Generation?
Retrieval-Augmented Generation (RAG) is a novel technique in the field of artificial intelligence that combines information retrieval capabilities with text response generation. It can be likened to combining ChatGPT with a dedicated knowledge base.
Court of Artificial Intelligence
To understand how this technology works, let us imagine a courtroom. The judge represents a language model (LLM) who has a broad, general knowledge of the law, just as the LLM has a general knowledge of the world and current events. Each new court case is like a new query from the user to the AI system.
When a judge is faced with a complex case requiring specialist knowledge, a judicial assistant steps in. This assistant symbolises the 'retrieval' process in RAG technology. His task is to search the extensive law library (our knowledge base) for relevant precedents and detailed information.
Having found the relevant documents, the assistant delivers them to the judge. This stage is responsible for retrieving the most pertinent information from the knowledge base. The judge analyses the provided material in the context of the case, combining his/her general legal knowledge with the detailed information from the documents. He or she finally issues a judgment, which is a combination of general understanding of the law and the specific facts of the case.
This process corresponds to the response generation stage of the AI model, where it combines its general knowledge with the information obtained. Just as a judge cites specific laws and precedents, the algorithm can cite sources of information, increasing the credibility of its answers.
How exactly does this technology work?
Retrieval
The system searches an extensive database or collection of documents.
Transformation of the query
The user's query is converted into a numerical format, called a vector.
Comparison of the vectors
The system finds the most relevant information by comparing vectors.
Response generation
The AI model combines the information found with its own knowledge to create the final answer.
Examples of the use of RAG in various sectors
The applications of this technology are extremely broad. We can imagine medical assistants supporting doctors with the latest research, financial analysts using up-to-date market data or advanced customer support systems. The technology is also used in employee training and productivity tools for developers.
HVAC
In the HVAC (Heating, Ventilation, and Air Conditioning) industry, RAG can be applied to Product Information Management (PIM) systems. For example, a ventilation product catalogue can contain thousands of technical specifications, installation instructions and service documentation. RAG makes it possible to quickly and precisely search for information on a specific fan model, its technical specifications, compatibility with other equipment and available spare parts. This enables technicians and dealers to quickly access the information they need, making customer service and service more efficient.
Health and Beauty Industry
In the Health and Beauty industry, RAG can act as an assistant with comprehensive knowledge about product ingredients, possible side effects, and appropriate uses. For example, customers looking for a facial cream can ask about ingredients suitable for their skin type or potential allergens. RAG can also provide information on clinical studies, other users' opinions, and dermatological recommendations, increasing purchase confidence and customer satisfaction.
Construction and DIY Industry
In the construction and DIY industry, RAG can assist in selecting products for specific applications or styles. For example, if a customer wants to renovate a bathroom and is looking for tiles, RAG can suggest products that match the chosen style, color, and budget. Similarly, if a customer is searching for paving stones for their garden, RAG can provide information on different types of stones, their durability, installation methods, and compatibility with other landscaping elements.
RAG on your own product data
In a company, the knowledge base for RAG is usually the PIM and the ERP. The PIM holds product facts such as names, descriptions, attributes and allergens, the ERP holds prices and stock. An assistant built on RAG answers with this current data instead of what the language model remembers.
For customers, installers and sales
The assistant answers questions about specifications, availability and installation, suggests the parts an installation still needs and can add products to a B2B order or a shopping cart.
For employees
The same assistant helps new employees learn procedures and products, and lets everyone search documents, reports and procedures in plain language.
LemonHub.AI includes an assistant built on RAG that answers from PIM and ERP data. See also AI assistants and agents on product data.
RAG offers many advantages for AI systems
- Increased accuracy and reliability of responses
- Ability to cite sources, building user trust
- Flexibility to update the knowledge base
- Reducing the phenomenon of 'hallucinatory' AI
- Cost-effectiveness: Does not require continuous training of the model
- Data security: Data are not shared with language models
Verdict
Technology combines the power of language models with the precision of information retrieval systems. It opens up new possibilities for the processing and use of knowledge, promising more intelligent, reliable and useful AI systems in the future. As in the judicial analogy, technology allows AI systems to make 'judgements' that are both wise and precise, based on broad knowledge and specific facts.
Read more: AI-powered product recommendations and AI use cases in PIM.
