Large language models (LLMs)

Discover universal JSON prompt templates for extraction, generation, and analysis, plus best‑practice automation and error‑handling techniques for production‑grade AI pipelines.

In this tutorial, learn how to set up a local AI co-pilot in Visual Studio Code using IBM Granite 4, Ollama, and Continue, overcoming common enterprise challenges such as data privacy, licensing, and cost. The setup includes open-source LLMs, Ollama for model serving, and Continue for in-editor AI assistance.

In this tutorial, you learn how to create a simple RESTful Java AI application that asks a large language model (LLM) to write a short poem based on a topic provided by the user.

In this article, we demonstrate using different prompts with different contexts to analyze how LLMs behave when asked generic-related questions. We show how the Granite 3.3-2b model is good at identifying associated quantifiers in generic phrases and the model's responses are context-driven too.

Learn how to use external LLMs with watsonx Orchestrate AI Gateway, which allows for more flexibility and choice in building AI-powered agents.

Learn AgentOps in watsonx Orchestrate with IBM Telemetry and Langfuse for AI observability, agent monitoring, debugging, and performance optimization.

InstructLab empowers developers to unleash the full potential of LLMs, offering a streamlined training process, cost-efficiency, community collaboration, and stability in model performance.

In the rapidly evolving landscape of AI development, large language models (LLMs) have become powerful tools for creating intelligent applications. However, a persistent challenge has been connecting these models to the diverse data sources they need to provide context-aware responses. Today, this integration becomes dramatically simpler by combining Model Context Protocol (MCP) and GraphQL.

Introducing Data Prep Kit (DPK) as a streamlined data preparation tool for LLMs

Boost mainframe DevOps with AI tools such as watsonx Code Assistant for Z and IBM Test Accelerator for Z for COBOL refactoring, Java transformation, automated testing, and z/OS modernization.