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Generate Insights with LLMs

Coursera PlusMonthly 3 个月课程4 折优惠 ，让你轻松掌握闪耀技能。立即节省

Generate Insights with LLMs

本课程是 AI Techniques, Causal Inference & Business Optimization 专项课程的一部分

位教师：Hurix Digital

包含在中

了解更多

4个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

4 小时完成

灵活的计划

自行安排学习进度

4个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

4 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Measure and improve LLM output quality using metrics like ROUGE and BLEU to systematically enhance executive business communications.
Design scalable data-to-text pipelines by integrating SQL data sources, Python processing, and LLM APIs for automated reporting.
Apply human-in-the-loop evaluation to complement automated metrics and assess relevance, clarity, and real business value of outputs.
Make cost-performance trade-offs by comparing open-source and commercial LLMs based on cost, latency, and enterprise needs.

您将获得的技能

您将学习的工具

要了解的详细信息

可分享的证书

添加到您的领英档案

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

本课程是 AI Techniques, Causal Inference & Business Optimization 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

向行业专家学习新概念
获得对主题或工具的基础理解
通过实践项目培养工作相关技能
获得可共享的职业证书

该课程共有4个模块

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.

单元详情

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

涵盖的内容

2个视频1篇阅读材料2个作业

2个视频总计10分钟

Building Effective Prompts for Executive Brief Generation7分钟
Demonstrating LLM Brief Generation with OpenAI API3分钟

1篇阅读材料总计12分钟

LLM-Powered Executive Brief Generation: Foundations and Quality Metrics12分钟

2个作业总计21分钟

Build Your First LLM Executive Brief Generator15分钟
Executive Brief Generation Assessment6分钟

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.

涵盖的内容

3个视频1篇阅读材料1个作业1个非评分实验室

3个视频总计15分钟

The Power of Automated Data Storytelling4分钟
SQL to Python to LLM: Pipeline Component Integration7分钟
Building Your First Data-to-Text Pipeline5分钟

1篇阅读材料总计10分钟

Architecture and Components of Data-to-Text Pipelines10分钟

1个作业总计7分钟

Data-to-Text Pipeline Assessment7分钟

1个非评分实验室总计18分钟

Building Production-Ready Sales Intelligence Pipeline18分钟

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

涵盖的内容

2个视频2篇阅读材料2个作业

2个视频总计11分钟

Human Evaluation Methodologies for LLM Fine-tuning Assessment7分钟
Fine-tuning a Small LLM on Company FAQ Data4分钟

2篇阅读材料总计16分钟

Fine-tuning Methodologies and Human Evaluation Frameworks11分钟
Podcast: From Generic to Domain-Specific: LLM Customization Success Stories5分钟

2个作业总计23分钟

Design and Execute LLM Fine-tuning Evaluation Study17分钟
LLM Fine-tuning and Evaluation Assessment6分钟

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