Introduction and Foundations
Module 1:
Introduction to DeepSeek Coder
Understand what DeepSeek Coder is
Learn about the architecture and model type
Compare with other Code LLMs like Codex, CodeLlama, and StarCoder
Identify key use cases: code generation, explanation, translation, debugging
Module 2: Setup
and Access
How to access DeepSeek Coder via HuggingFace, API, or local instance
Install necessary Python packages
Run your first code completion example
Module 3:
Prompt Engineering for Coding Tasks
Learn the structure of a good code prompt
Write prompts for generating functions, explaining code, and translating
languages
Test variations in prompting and analyze results
Module 4:
Performing Specific Tasks
Use prompts for automatic documentation
Use prompts for bug detection and resolution
Use prompts to refactor messy or inefficient code
Advanced Usage and Customization
Module 5:
Real-World Use Cases
Explore how to integrate DeepSeek Coder in real development environments
Understand automation in CI/CD workflows
Explore use in VSCode or notebooks for live assistance
Module 6:
Advanced Prompting
Apply few-shot prompting to demonstrate complex code tasks
Use Chain of Thought prompting to improve logical reasoning in generated code
Simulate peer review or pull request feedback via prompts
Module 7:
Evaluation and Limitations
Learn about ways to evaluate model output
Discuss strengths and weaknesses of DeepSeek Coder
Explore ethical considerations in LLM-based coding
Module 8:
Extensions and Future Use
Compare with other emerging models
Explore ideas for customization or fine-tuning
Discuss multi-agent use cases for collaborative AI coding
Final Lab and Evaluation
ProgNXT Certification
