In modern data-driven organizations, analysts often spend significant time diagnosing metric fluctuations, drafting insight narratives, and tailoring summaries for executives, product managers, and business stakeholders. Across domains like e-commerce, fintech, SaaS, and logistics, hundreds of KPIs – such as user growth, churn segmentation, benefit adoption, or engagement share – are reviewed weekly. This process is labor-intensive, inconsistent, and often misaligned with real-time decision-making needs.

This talk introduces a production-ready implementation that leverages Large Language Models (LLMs) to automate weekly insight generation. We present a three-phase strategy incorporating prompt engineering, retrieval-augmented generation (RAG), and domain-specific fine-tuning for scalable, contextualized summarization.