The blog post shows integrating watsonx Assistant and watsonx.ai to create a full-screen user interface for interacting with a large language model (LLM) using minimal coding. It outlines the motivation, architecture, setup process, and specific actions necessary to deploy the integration on IBM Cloud Code Engine.
AI Prompt Engineering: Streamlining Automation for Large Language Models
This blog post focuses on the importance of Prompt Engineering in AI models, particularly Large Language Models (LLMs), for reducing manual effort and automating validation processes. It emphasizes the need for automation to handle increasing test data and variable combinations, and discusses the use of the Watsonx.ai Prompt Lab for manual and initial automation processes. The post also highlights the significance of integrating automation with version control for consistency and reproducibility.
How to create a watsonx.ai REST client in Spring Boot?
This blog post demonstrates the Java Spring Boot implementation to invoke a watsonx.ai endpoint. It outlines the classes and steps involved, including building and sending requests, handling prompts, and extracting answers. The post also provides sample code for invoking the endpoint and using RestTemplate. Overall, it offers a comprehensive guide on utilizing watsonx.ai in a Spring Boot application.
