The Rise of Generative AI: Creating Competitive Advantage | Bartek Roszak, STXNext

Generative AI, particularly retrieval augmented generation (RAG), is in high demand as companies seek to leverage its capabilities to improve operational efficiency and enhance customer experiences. - Building a successful generative AI project requires a combination of engineering and research skills, as well as a deep understanding of the specific use case and data sources involved. - Ongoing maintenance and monitoring are crucial for ensuring the continued success of generative AI projects, as models need to be regularly updated and evaluated to maintain accuracy and relevance.

 

Today we sit with Bartek Roszak, who is the head of AI at STXNext, about the integration of ethical AI practices and compliance. Bartek shares his journey in AI, starting as a stock trader and transitioning to data science and deep learning. He discusses the evolution of STXNext from a Python development house to an AI-powered company and the growing demand for generative AI solutions. Bartek explains the concept of retrieval augmented generation (RAG) and its applications in various industries. He also highlights the importance of prompt engineering and the challenges of maintaining AI models post-deployment.

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Founder Bio:

Bartek Roszak is a luminary in the AI sphere and the driving force behind STXNext’s AI strategy. With significant roles and achievements in the field of AI, Bartek has a background in stock trading and transitioned to data science and deep learning. He has experience in building AI solutions for various industries and specializes in generative AI, particularly in the area of retrieval augmented generation (RAG). Bartek is passionate about integrating AI into business operations and helping companies leverage AI for competitive advantage.

Time Stamps:

02:26 Bartek’s background and transition into AI 04:51 Evolution of STXnext into an AI-focused company 07:12 Popular AI use cases and interest in generative AI 10:07 What is retrieval augmented generation (RAG): Use cases and interest 12:51 External uses of generative AI with high demand 14:48 Skills required to build and deploy gen AI products 16:38 Minimum experience with LLMs or machine learning needed to work with Gen AI 19:49 The importance of quality assurance in machine learning projects 21:43 Importance of having skillsets in prompt engineering. 23:19 Diversifying LLM base to mitigate downtime risks 24:52 Creating a competitive advantage by using LLM as part of a larger system 28:04 The process of working with clients on AI use cases 32:51 Post-project maintenance and client involvement 34:23 Transitioning the project to the client’s data science team 36:40 STX Next’s plans for 2024

Resources

Company website: https://www.stxnext.com/ Twitter: https://twitter.com/STXNext LinkedIn: https://www.linkedin.com/company/stx-next-ai-solutions/ Instagram: https://www.instagram.com/stx_next/