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Antoni Kozelski
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Antoni Kozelski is an AI entrepreneur and the founder and CEO of Vstorm, a company specializing in custom AI and LLM-based software development.24 He founded Vstorm in October 2017 with the mission to help companies ethically implement AI, enabling growth and innovation.2
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Antoni Kozelski is committed to leveraging AI to boost company growth for entrepreneurs and intrapreneurs, while also documenting his entrepreneurial journey and sharing insights with others in the field.4
Highlights
What is RAG? - Retrieval-Augmented Generation
It’s a language model but on steroids. One of the AI solutions for your company
#RAG, or Retrieval-Augmented Generation, is an #artificialintelligence #technology that combines the processes of information retrieval with text generation. This model consists of two main components:
𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐞𝐫: This component is responsible for finding relevant information that can be useful in responding to a user's question. The retriever typically searches a large database or a collection of documents to identify text fragments that are most relevant to the posed query.
𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐨𝐫: After receiving information from the retriever, the generator uses this data to create a coherent and meaningful response. The generator usually relies on machine learning technologies, such as neural networks, to construct responses based on the selected text fragments.
The RAG model is particularly useful in situations where responding to a question requires understanding and integrating information from multiple sources, a challenge for traditional generative models that rely only on a model's internal knowledge.
𝐈𝐧 𝐰𝐡𝐢𝐜𝐡 𝐢𝐧𝐝𝐮𝐬𝐭𝐫𝐲 𝐑𝐀𝐆 𝐜𝐚𝐧 𝐛𝐞 𝐚𝐩𝐩𝐥𝐢𝐞𝐝?
In all. Due to its universality, every industry can benefit.
𝐇𝐨𝐰 𝐭𝐨 𝐢𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭 𝐑𝐀𝐆 𝐬𝐢𝐦𝐩𝐥𝐲?
Use it together with GPT (Generative Pre-trained Transformer). By pairing RAG with GPT, you can enhance your system's ability to handle complex questions more effectively. This combination uses the strengths of both technologies: finding relevant information and turning it into clear answers.
RAG represents a significant step forward in making AI more adaptable and insightful across diverse industries. Combining retrieval with generation, ensures more accurate and contextually appropriate responses, enhancing both customer interaction and decision-making processes in any sector.
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