Tag: prompt engineering
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watsonx.ai Studio and Prompt Lab, Your First Governed Prompt (IBM Gen AI Series, Part 2)
The watsonx.ai studio and Prompt Lab, hands on: projects and the Runtime service, Chat mode after the 2026 removal of Structured and Freeform, decoding parameters, and how prompt length drives your token bill.
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Amazon Bedrock Prompt Management, Flows, and Prompt Caching (AWS Gen AI Series, Part 15)
Prompt caching, Prompt Management, and Bedrock Flows get grouped together and confused constantly. What each one does, what caching actually saves, and which to reach for.
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Fine-Tuning vs RAG vs Prompting: Which One, and When (GenAI Series, Part 15)
Prompting steers, RAG adds facts, fine-tuning changes behaviour. The one question that decides which to use, a side-by-side comparison, and why to escalate in order of cost.
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Prompt Engineering That Actually Works (GenAI Series, Part 12)
Prompt engineering is not secret incantations, it is clear communication. The four moves that do most of the work, system vs user prompts, and the anti-patterns that waste tokens.
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About the Author
Dr Pranay Jha
Dr. Pranay Jha is a Cloud and AI Consultant with 18+ years of experience in hybrid cloud, virtualization, and enterprise infrastructure transformation. He specializes in VMware technologies, multi-cloud strategy, and Generative AI solutions. He holds a PhD in Computer Applications with research focused on Cloud and AI, has published multiple research papers, and has been a VMware vExpert since 2016 and a VMUG Community Leader.
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