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Stephen C. Webster
AI consultant, prompt engineer, journalist, entrepreneur | ex-Accenture, consulted 2x with Google | former editor of Raw Story, The Progressive magazine, & Austin.com | UT/McCombs AI student
Stephen C. Webster is a Senior Consultant at Quantum Impact AI, where he leverages his extensive experience in technology and consulting to drive innovative solutions. He has a robust background in technology-driven strategies, having previously founded and served as President and CEO of SIS Holdings Group, LLC, a technology consultancy focused on digital transformation and managed services.
Professional Background
- Current Role: Senior Consultant at Quantum Impact AI
- Previous Experience:
- Founder & CEO: SIS Holdings Group, LLC (2010 - Present)
- Led initiatives in business analytics, cybersecurity, and cloud services.
- Achieved significant revenue growth and established partnerships with major tech firms like Amazon and Microsoft.
- CIO Whisperers Ambassador: Engaged with C-level executives to enhance technology leadership.
- Technical Manager: Dynegy; Sr. International Systems Coordinator: Enron.
- Founder & CEO: SIS Holdings Group, LLC (2010 - Present)
Education
- Bachelor's Degree: Computer Science from the University of Texas
- MBA: Finance from the University of Houston, C.T. Bauer College of Business
Achievements
Stephen has been recognized multiple times for his contributions to the tech industry, including:
- Inclusion in CRN’s “Next-Gen 250 List” for groundbreaking technology companies.
- Ranking as the #2 Fastest Growing Tech Company by the Houston Business Journal in 2012.
Skills and Expertise
His expertise spans various domains including:
- Digital Transformation
- Cybersecurity
- IT Strategy
- Business Analytics
This diverse skill set positions him well to consult on advanced AI solutions at Quantum Impact AI.
Highlights
To RAG (retrieval augmented generation), or not to RAG? That is the question this Deepmind study asks.
They find that an unoptimized RAG with a document "chunking" strategy is the most cost effective genAI solution for companies with large datasets... BUT, large-token LLM prompts for tasks like summarizing complex documentation are much more accurate, albeit most costly.
Choosing between the two methods just depends upon what your users are doing most.
If you've ever wanted to learn Python, and I definitely have some friends who have, check out my latest Custom GPT. I call it "TutorBot," and it's highly useful in three very different fields: Python, US History, and math. I specifically trained it and iterated on the prompt to be highly versatile at logic tasks in very different realms, all in a single shot, no circle of experts needed. I'm quite proud of the result. :)
