Automate The Boring Stuff With LLMs

Code and slides to accompany the online webinar by Data For Science.

LLMs are good at a surprising amount of the tedious work that fills a normal week — pulling structured fields out of free text, summarizing long documents, running multi-step reviews, and answering questions over a knowledge base. This webinar walks through four end-to-end Jupyter notebooks that show how to build each of those workflows with LangChain, CrewAI, and Anthropic's Claude, with practical guardrails, schemas, and evaluation patterns you can lift straight into your own projects.

Contents

This tutorial is divided into four parts:

1. Structured Extraction

• LLM Review
• LangChain Deep dive
• Pydantic schema planning
• JSON validation loop
• Prompt guardrail patterns

Notebook: 1. Structured Extraction.ipynb Turn free-form job descriptions into validated, typed records using a Pydantic schema and LangChain's PydanticOutputParser.

2. Summarization

• Map-reduce chain demo
• Action-item templates
• Router prompts
• Misclassification

Notebook: 2. Summarization.ipynb Map-reduce summarization of long arXiv papers, plus a structured action-item template for downstream automation.

3. Agents

• Agent review
• CrewAI DeepDive
• Researcher/Writer pipeline
• Synchronous and Asynchronous crews
• Compliance checklist creation

Notebook: 3. Agents.ipynb A CrewAI multi-agent pipeline that audits real privacy policies against a GDPR/CCPA compliance checklist.

4. ChatBot

• Embeddings
• Vector Databases
• Retrieval Augmented Generation
• Tool integration
• Interactive agent

Notebook: 4. ChatBot.ipynb A laptop-friendly RAG chatbot over a Wikipedia subset, using local sentence-transformer embeddings, Chroma, and tool use.

Slides for the webinar live in slides/AutomateTheBoringStuff.pdf.

References

• Hands-On Large Language Models
• AI Engineering: Building Applications with Foundation Models
• LLM Engineer's Handbook
• Building LLM Powered Applications
• Prompt Engineering for LLMs
• LLMs in Production
• Agentic Design Patterns
• Generative AI with LangChain
• AI Agents in Action
• Building AI Agents with LLMs, RAG, and Knowledge Graphs

Setup

Install uv if you don't have it yet:

curl -LsSf https://astral.sh/uv/install.sh | sh

Install dependencies and launch Jupyter:

uv sync
uv run jupyter notebook

Set your Anthropic API key before running the notebooks:

export ANTHROPIC_API_KEY=sk-ant-...

Author

Bruno Gonçalves Data For Science, Inc. Web: www.data4sci.com Twitter/X: @bgoncalves LinkedIn: @bmtgoncalves Email: info@data4sci.com Schedule a Call: https://data4sci.com/call