GenAI for Software Engineers: Prompt Engineering and Fine-Tuning
Master prompt engineering and fine-tuning techniques to revolutionize your software development workflows with Generative AI.
...
Share
Generative AI Fundamentals for Software Engineers
Unit 1: GenAI Model Architectures
Intro to GenAI Models
GANs: The Creative Duel
VAEs: Encoding Reality
Diffusion Models: Noise to Signal
Transformers: Attention, Please!
Unit 2: GenAI Use Cases in Software Engineering
GenAI for Code Gen
Docs with GenAI
Testing with GenAI
Debugging with GenAI
Other GenAI Use Cases
Unit 3: Setting Up Your GenAI Environment
Python for GenAI
Cloud vs. Local GenAI
Jupyter Notebooks
GPU Setup
Your First GenAI Project
Unit 4: Interacting with Pre-trained Models
Intro to APIs and SDKs
Authentication
Making Your First Request
Prompt Engineering for Software Engineering Tasks
Unit 1: Fundamentals of Prompt Engineering for Code
Intro to Prompting Code
Crafting Basic Prompts
Few-Shot Learning
Prompting Frameworks
Prompting Best Practices
Unit 2: Advanced Prompting Techniques for Code
Chain-of-Thought
Self-Consistency
Prompting for Refactoring
Prompting for Bug Fixes
Prompting for Security
Unit 3: Prompting for Documentation and Debugging
Doc Generation Prompts
API Spec Prompts
Debugging Prompts
Vulnerability Prompts
Prompt Evaluation
Fine-Tuning GenAI Models for Software Engineering
Unit 1: Data Preparation for Fine-Tuning
Dataset Essentials
Data Cleaning 101
Doc Data Wrangling
Bug Report Refinement
Train/Val/Test Splits
Unit 2: Fine-Tuning Strategies
Fine-Tuning Overview
Setting Up for Success
Choosing Your Model
The Hyperparameter Hunt
Training Time!
Unit 3: Evaluation and Optimization
Eval Metrics Deep Dive
Overfitting? No Thanks!
Parameter Tweaks
Generalization Magic
Putting It All Together
Integrating and Evaluating GenAI in Software Workflows
Unit 1: GenAI Integration Strategies
Workflow Integration
CI/CD Pipelines
API Considerations
Real-World Examples
Tooling & Frameworks
Unit 2: Automating Code Review with GenAI
GenAI for Code Review
Static Analysis
Security Vulnerabilities
Code Style & Conventions
Reviewer Assistance
Unit 3: Ethical Considerations and Responsible Use
Ethical Implications
Bias Detection
Security Risks
Responsible AI
Transparency & Explainability