Live-demo showing automated testing of large language models. Addresses non-determinism in ML systems and demonstrates how a second LLM can act as a judge. Also explores Retrieval Augmented Generation (RAG) for querying documents and guiding tests.

Put your prompt-writing skills to the test in a friendly, fast-paced contest. You’ll work with a ring-fenced large language model (LLM) and a shared dataset, racing to surface the right answers as the questions get tougher. Think of it as a pub quiz for data folk – but the questions are answered with code-like prompts. Quick briefing – we’ll show you the dataset, the rules and a few prompt-engineering tips. Answer the questions – each round ups the difficulty, challenging you to refine, chain or re-use prompts in inventive ways. Leaderboard & prizes – points for accuracy and ingenuity. Top spot takes home bragging rights and a tidy prize.

Hands-on bootcamp to build a working QA Agent from scratch. 8 live instructor-led sessions (weekends only) designed to help testers, QAs, and SDETs move beyond theory and actually build their own functional QA Agent from scratch. By the end you’ll know how to design and implement AI-driven QA workflows that assist with bug triage and prioritization, generate and organize test reports automatically, run and manage complex testing workflows, and take over repetitive tasks. Tools covered include LangChain, Streamlit, Postman, Playwright, Memory, Prompting, and Tool Use. Format: 8 live sessions, 3 hours each, instructor-led with real coding and projects.

Hands-on exercises comparing weak vs. strong prompts

Deep-dive into Building BAML, a programming language for LLM prompting, with live coding and production use cases across chatbots, healthcare ERP, and finance.