deep learning models that generate high-quality content
Generative AI refers to deep-learning models that can generate high-quality text, images, and other content based on the data they were trained on. These deep learning models can take raw data and “learn” to generate statistically probable outputs when prompted.
Learn how to build a high‑accuracy GraphRAG agent using watsonx Orchestrate, watsonx.ai embeddings, and DataStax Astra DB with this hands‑on developer tutorial that covers vector search, knowledge graphs, web crawling, semantic retrieval, and agentic AI workflows.
In this step-by-step tutorial, learn how to build and deploy a Contract Research agent for legal document automation with IBM watsonx Orchestrate, Box MCP, and AI tools.
Learn how fine‑tuning a small language model (Mistral 7B) and using a Granite 3.3 8B orchestrator solves the consistency challenges of large‑scale business document evaluation.
Learn how to build an event-driven agentic AI system using Confluent Cloud and watsonx Orchestrate. Learn how to consume events from a Kafka topic using a watsonx Orchestrate agent, and configure the agent to analyze incoming events as they arrive. To add business context, the agent also references a set of documents, such as policies, procedures, or reference material, and correlates them with event data when generating insights.
Learn how Model Context Protocol enables scalable multi agent AI systems through client, server, and hybrid architecture patterns, LLM placement strategies, reusable AI agents, dynamic orchestration, enterprise design trade offs, and real world implementation guidance.
Build a simple intelligent virtual agent (IVA) system powered by IBM Watson Speech and Genesys Bot by integrating voice and channels for an IBM watsonx Orchestrate agent.
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