Learning Path
AI-Assisted Developer Path
Boost developer productivity with ChatGPT, GitHub Copilot, Claude Code, and modern LLM development workflows.
Skills You'll Master
- AI-Assisted Coding
- GitHub Copilot
- Claude Code
- LLM App Development
Interactive Roadmap
Explore Courses Step by Step
Click any step below or use the navigation arrows. Use keyboard ← → to move between courses.
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Step 1 · AI for Developers
ChatGPT: AI Tool for Software Developers
UdemyCourseBeginner
Boost software development productivity with ChatGPT and advanced prompt techniques.
Boost software development productivity with ChatGPT and advanced prompt engineering techniques.
aichatgptdevelopment
Hands-on Practice
Hands-on Labs
Apply what you learn with curated labs that reinforce the skills in this path. Complete them in any order that fits your schedule.
UdemyLabIntermediate
Connect OpenAI Model to External Tools using Function Calling
Hands-on lab: implement OpenAI function calling to let a language model invoke external APIs and return structured, tool-augmented responses.
UdemyLabIntermediate
Managing Cross-Cutting Concerns in OpenAI Programming
Hands-on lab: implement retry logic, error handling, logging, and rate-limit management for production-ready OpenAI API integrations.
UdemyLabIntermediate
Building a Weather Report Generator with LangChain
Hands-on lab: use LangChain components — models, prompts, and output parsers — to build an automated weather report generator.
UdemyLabIntermediate
Building a Conversational App using LangChain Expression Language (LCEL)
Hands-on lab: compose LangChain chains using LCEL syntax to build a conversational application with memory and streaming support.
UdemyLabIntermediate
Building a RESTful Weather API using LangChain and LangServe
Hands-on lab: expose a LangChain chain as a REST API using LangServe, enabling HTTP-based invocation of LLM-powered endpoints.
UdemyLabIntermediate
Using Few-Shot Prompting to Build a Weather Forecast Summarizer
Hands-on lab: design few-shot prompt templates in LangChain to guide an LLM to produce consistent, example-driven weather summaries.
UdemyLabIntermediate
Writing Effective Prompts Using Directions, Formats, and Examples
Hands-on lab: craft effective prompts by applying directions, output format constraints, and few-shot examples to get reliable LLM responses.
UdemyLabIntermediate
Structuring LLM Outputs with LangChain and Pydantic
Hands-on lab: use LangChain output parsers with Pydantic models to enforce structured, type-safe JSON outputs from language models.
UdemyLabIntermediate
Build a Dynamic Weather App with Function Calling in LangChain
Hands-on lab: integrate LangChain function calling tools to dynamically invoke external weather APIs based on LLM reasoning.
UdemyLabIntermediate
Build a Weather App using the 'Least to Most' Prompting Technique
Hands-on lab: apply the Least-to-Most prompting technique to break complex weather queries into smaller sub-problems solved sequentially by an LLM.
UdemyLabIntermediate
Summarize Text using the 'Few Shot' Prompting Technique
Hands-on lab: use few-shot prompting to guide an LLM in generating concise, structured summaries from weather text data.
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