Getting Started with Windsurf AI Course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview: This concise, hands-on course guides you through building and deploying AI workflows with Windsurf AI in under four hours. You'll progress from setup to deployment, working with real data and LLMs in an interactive browser environment. Each module combines focused instruction with immediate coding exercises, culminating in a complete, monitorable pipeline you can reuse and share.
Module 1: Introduction to Windsurf AI
Estimated time: 0.5 hours
- Overview of Windsurf AI
- Understanding use cases for AI workflows
- Installation and environment setup
- Exploring example repositories
- Verifying Windsurf installation
Module 2: Data Ingestion & Preprocessing
Estimated time: 1 hour
- Loading CSV, JSON, and text data
- Integrating data sources into Windsurf
- Filtering and transforming data
- Normalizing text and structured inputs
- Outputting cleaned DataFrames
Module 3: Prompt Chains & LLM Integration
Estimated time: 1 hour
- Defining prompt templates
- Chaining multiple LLM calls
- Handling and parsing LLM responses
- Summarizing text with LLMs
- Translating and extracting entities
Module 4: Custom Functions & Branching Logic
Estimated time: 1 hour
- Embedding Python functions as workflow nodes
- Implementing conditional branches
- Adding loops and error handling
- Routing logic based on sentiment analysis
- Managing workflow state
Module 5: Monitoring, Visualization & Logging
Estimated time: 0.5 hours
- Accessing the built-in dashboard
- Capturing logs during execution
- Collecting performance metrics
- Visualizing workflow execution graphs
Module 6: Deployment & Reuse
Estimated time: 0.5 hours
- Packaging workflows as CLI tools
- Exporting pipelines for production
- Running pipelines on new inputs
Prerequisites
- Familiarity with Python programming
- Basic understanding of LLM concepts
- Experience with data handling in Python
What You'll Be Able to Do After
- Install and configure Windsurf AI for local development
- Ingest and preprocess structured and unstructured data
- Design and execute multi-step LLM pipelines
- Implement conditional logic and error handling in workflows
- Monitor, visualize, and deploy reusable AI pipelines