The Rise of Generative AI: Creating Competitive Advantage | Bartek Roszak, STXNext
Release Date: 04/04/2024
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info_outlineGenerative 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.
If your company is looking to scale its AI initiatives, head over to Tesoro AI (www.tesoroai.com). We are experts in AI strategy, staff augmentation, and AI product development.
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/