Issued 21 July 2026
Hard Skills: Taxonomy Discovery: Mining unstructured historical text to create a mutually exclusive and collectively exhaustive intent taxonomy. Hybrid Data Curating: Combining human-labeled and LLM-labeled data to create high-quality 'golden' evaluation sets to ground LLM Judges. Confidence Monitoring: Using LLM confidence status to monitor the coverage of the intent taxonomy across customers problems. Learnings from Productionising LLM-powered Use Cases: Navigating the real-world LLM decommissioning cycles, and what this means for performance, latency, and cost. Soft Skills: Methodological Pragmatism: Deciding when to use automated LLM labelling and evaluation versus when human verification is required. Managing Ambiguous Edge Cases
Having completed this session, you will be equipped with the skills and knowledge to effectively leverage Large Language Models (LLMs) to transform text data into structured insights.
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