Before learning about prompt engineering, I assumed that interacting with AI was as simple as asking a question and receiving an answer. While that is true to some extent, I soon discovered that the quality of the response often depends on how the prompt is written. Through the Craft Precise...
Explore IBM Netezza's native vector capabilities — including vector storage, similarity search, and distance operators built for AI, machine learning, and RAG applications. Details As AI and generative AI workloads grow in importance, vector search has become a critical capability...
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When I first started learning about Artificial Intelligence, I believed it was a field primarily for programmers, data scientists, and technology professionals. Coming from a non-IT background, I was unsure whether I could understand the concepts or keep pace with the rapidly evolving world of...
When many people hear the words “Artificial Intelligence,” they immediately think it is only for programmers, data scientists, or technology experts. I used to think the same way. However, after starting my learning journey, I realized that AI can also be explored by beginners, students, and...
Wrapping up my series on decision agents, here’s the third post. How AI Agents and Decision Agents Combine Rules & ML in Automation Building Decision Agents with LLMs & Machine Learning Models Designing AI Decision Agents with DMN, Machine Learning & Analytics [this...
From Simple Instructions to Precision and Guardrails: The Journey of Engineering Production-Ready Prompts for IBM CDC Part 4: Key Lessons Learned and Best Practices for Prompt Engineering Authors: HS Manoj Kumar, Dev Sarkar Recap: The Transformation Complete In Parts...
From Simple Instructions to Precision and Guardrails: The Journey of Engineering Production-Ready Prompts for IBM InfoSphere Change Data Capture Part 3: The Evolution of the Prompt: From Simple to Complex Authors: HS Manoj Kumar, Dev Sarkar Recap: From Failures to...