Tag: IBM watsonx
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InstructLab on watsonx, from Taxonomy to Aligned Model (IBM Gen AI Series, Part 12)
InstructLab teaches a Granite model new skills from a handful of hand written examples, using a taxonomy, synthetic data generation, and phased training. Here is how it works on watsonx.ai and when to reach for it.
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Prompt Tuning and Fine-Tuning Granite on watsonx (IBM Gen AI Series, Part 11)
watsonx.ai gives you prompt tuning, LoRA, and full fine tuning for Granite. Here is how each method works and how to pick the right one for the size of your data.
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Granite Guardian and Hallucination Detection on watsonx (IBM Gen AI Series, Part 10)
Granite Guardian is a judge model that scores watsonx answers for hallucination. Here is where it sits in a RAG pipeline, how groundedness detection works, and where to put your block threshold.
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RAG on watsonx.data with Milvus Vector Search (IBM Gen AI Series, Part 9)
How to build governed RAG on watsonx.data with the embedded Milvus vector database, from choosing a slate or Granite embedding model to sizing the service and picking an index.
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watsonx.ai Inferencing and Prompt Engineering, from API to Streaming (IBM Gen AI Series, Part 8)
How to call watsonx.ai foundation models: the generation and chat endpoints, the decoding parameters that steer output, streaming, Prompt Lab, and prompt template assets you can reuse.
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watsonx.ai Regions, Private Connectivity, and Security (IBM Gen AI Series, Part 7)
The watsonx.ai region you pick is a one-way door, and it quietly decides which models, tuning, and compliance you get. Here is how to choose it, then lock the network with private endpoints and your own keys.
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GPUs and Running watsonx.ai on OpenShift (IBM Gen AI Series, Part 6)
How watsonx.ai actually uses GPUs on Red Hat OpenShift: the operators that expose a card, why memory sizing decides everything, the single node rule for multi GPU models, and when MIG or time slicing is worth it.
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watsonx.ai Pricing, Resource Units, CUH, and the Plan Tiers (IBM Gen AI Series, Part 5)
watsonx.ai bills two meters at once, Capacity Unit Hours for compute and Resource Units for inference. Here is what each of the four SaaS plans costs, and the point where Essentials stops being the cheaper choice.
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watsonx.ai Deployment Options, From SaaS to Software on OpenShift (IBM Gen AI Series, Part 4)
watsonx.ai runs two ways: as a Service that IBM operates for you, or as software you run on Red Hat OpenShift. Here is how to pick, and what you own in each.
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IBM Granite Models, Third-Party Models, and Licensing on watsonx (IBM Gen AI Series, Part 3)
A plain walk through the IBM Granite 4.1 family, the third-party models sitting beside it in watsonx.ai, and why Apache 2.0 and uncapped IP indemnity, not benchmarks, usually decide which model an enterprise ships.
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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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IBM watsonx Generative AI Stack, End to End (IBM Gen AI Series, Part 1)
IBM watsonx explained end to end for beginners: how watsonx.ai, watsonx.data, and watsonx.governance fit together, where the Granite models sit, and what it costs to run.
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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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