Tag: RAG
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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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RAG: How to Stop Your AI Making Things Up (GenAI Series, Part 13)
Retrieval-augmented generation lets a model answer from your own documents by fetching the relevant passages at question time. How RAG works, and why it beats fine-tuning for facts.
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VMware Private AI Agent Builder: Composing Models, Knowledge Bases and Prompts (Private AI Series, Part 15)
Agent Builder in VMware Private AI Services lets you compose a model endpoint, a knowledge base and prompt instructions into a grounded agent. Here is what it actually does, where it sits, and where the agentic hype gets ahead of reality.
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Building a RAG Pipeline on VMware Private AI: 7 Failures That Quietly Break Retrieval (Private AI Series, Part 14)
Most RAG failures on VMware Private AI Foundation are not the LLM. Here are the seven pipeline failures that quietly wreck retrieval quality on PAIF 9, and how I fix each one in the field.
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Vector Databases in VMware Private AI: Running pgvector on Data Services Manager (Private AI Series, Part 13)
A reference-architecture look at the retrieval tier of VMware Private AI: where DSM-managed PostgreSQL with pgvector sits, how to place and size it, and whether to index with HNSW or IVFFlat.
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Architecture & Design
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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