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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 seasoned professional driving innovation and efficiency in the marketing department of a major drugmaker through the development of cutting-edge generative AI tools.

With over 20 years of experience in multimedia communications, Stephen leads and mentors a team of creative professionals, collaborates with cross-functional teams, and showcases the power of generative AI in creating high-quality content at remarkable speed and cost-efficiency.

Stephen's expertise includes prompt engineering, AI technologies, communications, digital marketing, enterprise LLM AI's, SaaS, BaaS, Python, and AI app development.

He has held various roles in ethical AI use case development, AI prompting, and AI-focused technical writing at prominent companies like Google, Kunai, e2f.ai, iMerit, and Aquent, emphasizing balanced political perspectives and safety measures.

As a former Editor in Chief at Raw Story, Stephen played a pivotal role in growing the platform into a leading digital news force, showcasing his ability to merge technology with impactful storytelling.

Having founded Proud Highway Media Group, LLC and contributed to multiple media outlets, Stephen has a strong background in building community-driven digital brands and mentoring individuals in the realm of digital media and climate activism.

Stephen holds a background in Liberal Arts and Sciences, General Studies, and Humanities from Blinn College, and a High School Diploma from Brenham High School.

Throughout his career, Stephen has demonstrated a unique ability to blend disruptive technology with human-centric approaches, fostering innovation within organizations of all sizes by leveraging social and emotional insights.

With experience across consulting, prompt engineering, journalism, and digital media entrepreneurship, Stephen is poised to drive transformative change within your organization through his diverse skill set and deep industry knowledge.

Highlights

Jul 29 · linkedin

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.

Jul 13 · linkedin

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. :)

Stephen C. Webster
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Location

Austin, Texas, United States