Build a production-ready multi-document agent with LlamaIndex, turning PDFs into retrieval and summarization tools using semantic selection for accurate answers.

Achieve GDPR-ready GenAI retention: classify prompts, outputs, and logs, configure OpenAI/Azure settings, and automate lifecycle deletion with audits and reporting.

Upgrade your RAG pipeline with proven chunking, MMR, metadata filters, and BM25/TF‑IDF choices to deliver sharper, trustworthy answers consistently.

Learn how to design a clear instruction hierarchy to prevent rule overrides, policy leaks, and output drift in production-grade systems.

Build your first Knowledge Graph RAG system step-by-step: model entities, embed meaning, and retrieve grounded, context-rich answers using Neo4j.

Lead teams adopting AI powered tools for software development using pilots, governance guardrails, and metrics proving productivity gains and impact.

Transform general-purpose LLMs into specialized, instruction-following models that understand your tasks, reduce token waste, and perform with precision.

Learn how to build a complete AI talent strategy that defines the right roles, identifies skill gaps, designs upskilling programs, and knows exactly when to hire.

Build production-ready multi-agent AI systems with CrewAI using reusable YAML-first patterns and explicit tools and tasks.

Build a fully functional LLM agent in Python with the ReAct pattern, complete with reasoning, actions, and automation.