watsonx.ai

A one-stop, integrated, end- to-end AI development studio

An enterprise-grade AI studio that includes a collection of the APIs, tools, models, and runtimes for developing and deploying AI services into your applications.

Tutorials

23 July 2025

Tutorial

Building AI applications with Model Context Protocol (MCP)

Model Context Protocol (MCP) represents a fundamental shift in how we build AI applications. In this comprehensive tutorial, we'll explore MCP and learn how to build a production-ready integration with IBM watsonx.ai, demonstrating how to create AI applications that can seamlessly connect to enterprise data and services.

23 May 2025

Tutorial

Vibe coding your way through expense reports

Learn how to automate expense reports using AI, natural language prompts, JSON, IBM watsonx.ai, large language models, and no-code vibe coding techniques.

13 May 2025

Tutorial

Building data pipelines using natural language with Data Prep Kit (DPK)

In this tutorial, we explore two experimental notebooks that demonstrate how agentic workflows can support the creation of data pipelines using the Data Prep Kit based on user input provided in natural language.

05 May 2025

Tutorial

Extract text from documents using watsonx.ai TextExtraction API

Using watsonx.ai TextExtraction API, you can efficiently extract text from PDFs and other documents that are highly structured and contain information in tables. You can extract and convert to a file format that is easier to work with programmatically, such as Markdown or JSON.

24 April 2025

Tutorial

Extracting, classifying, and summarizing documents using LLMs

To build a generative AI form filling tool, you can use a large language model (LLM) to extract, classify and summarize documents. In this tutorial, we walk you through a sample application that applies these techniques to a recipe website. For this example, we used three main AI tools to perform the tasks: Docling, watsonx.ai, and Langchain.

22 April 2025

Tutorial

Contributing knowledge to open-source LLMs like the Granite models using the InstructLab UI

Learn how to contribute to open source large language models (LLMs), such as the IBM Granite models, using InstructLab UI.

22 April 2025

Tutorial

Improve your RAG solution by converting natural language queries to Elasticsearch SQL by using watsonx.ai

In this comprehensive guide, you’ll learn exactly how to do both—combining the simplicity of SQL and the intelligence of watsonx.ai to handle everything from basic lookups to advanced text searches.

22 April 2025

Tutorial

Contributing knowledge to the open source Granite model using InstructLab

Learn how to contribute knowledge to open source large language models (LLMs), such as the Granite models, on your personal laptop or Mac device using the InstructLab v0.18.x environment.

03 April 2025

Tutorial

Deploying LLMs tuned with Parameter-Efficient Fine-Tuning (PEFT) techniques in watsonx.ai

IBM watsonx.ai can deploy models that have been fine-tuned with different methods. One such method is called Parameter-Efficient Fine-Tuning (PEFT), which is a powerful technique that enables the efficient adaptation of pre-trained models to specific tasks, without requiring significant computational resources or large amounts of training data. By using PEFT, you can adapt the model to your specific needs, while minimizing the risk of overfitting.

28 February 2025

Tutorial

Enhancing AI retrieval with HNSW in RAG applications

Learn how to use HNSW for fast, scalable AI retrieval in RAG pipelines, boost search efficiency, optimize embeddings, and improve large-scale AI applications.

19 February 2025

Tutorial

Create a LangChain RAG system in Python with watsonx

Learn how to create a LangChain RAG system in Python with watsonx. Fetch 27 articles from a website to create a vector store as context for an LLM to answer questions about the topic.

22 January 2025

Tutorial

Automate document analysis with highly intelligent agents

Integrate IBM watsonx with IBM Docling and open source CrewAI to automate document analysis with highly intelligent agents

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watsonx.ai

A one-stop, integrated, end- to-end AI development studio

Tutorials

Building AI applications with Model Context Protocol (MCP)

Vibe coding your way through expense reports

Building data pipelines using natural language with Data Prep Kit (DPK)

Extract text from documents using watsonx.ai TextExtraction API

Extracting, classifying, and summarizing documents using LLMs

Contributing knowledge to open-source LLMs like the Granite models using the InstructLab UI

Improve your RAG solution by converting natural language queries to Elasticsearch SQL by using watsonx.ai

Contributing knowledge to the open source Granite model using InstructLab

Deploying LLMs tuned with Parameter-Efficient Fine-Tuning (PEFT) techniques in watsonx.ai

Enhancing AI retrieval with HNSW in RAG applications

Create a LangChain RAG system in Python with watsonx

Automate document analysis with highly intelligent agents