Generate Insights with LLMs

Overview

Transform your data into strategic business intelligence with the power of large language models. This Short Course was created to help data analysts accomplish automated insight generation from complex datasets. By completing this course, you'll master practical LLM applications that turn raw data into compelling executive narratives, build automated reporting pipelines, and optimize model performance for real-world business scenarios. By the end of this course, you will be able to: Generate executive-ready briefs using tuned LLM prompts with measurable quality scores Build end-to-end data-to-text automation pipelines combining SQL, Python, and LLM APIs Fine-tune small language models and evaluate performance improvements through human assessment Conduct cost-benefit analysis comparing open-source and commercial LLM solutions This course is unique because it bridges the gap between technical LLM capabilities and practical business applications, focusing on measurable outcomes and real-world implementation challenges. To be successful in this project, you should have a background in Python programming, SQL queries, and basic understanding of API integrations.

Syllabus

Module 1: Executive Brief Generation - Foundation

Learners will apply LLMs to generate first-draft executive briefs that summarize model insights and refine prompts to meet specified ROUGE or BLEU scores.

Module 2: Data-to-Text Pipelines - Core Application

Learners will create comprehensive data-to-text pipelines that combine SQL, Python, and LLM APIs to automatically transform KPI (Key Performance Indicator ) tables into narrative summaries.

Module 3: LLM Fine-tuning & Evaluation - Integration

Learners will fine-tune small LLMs on company FAQs and measure improvement in response relevance through systematic human evaluation.

Module 4: Cost-Performance Analysis - Assessment

Learners will evaluate cost vs. latency trade-offs between open-source and commercial LLMs for real-time chat applications through systematic analysis.