AI for Software Engineers: Concepts and Techniques

Day 1: AI Foundations and Basic ML Concepts

Module 1: Introduction to AI in Software Development

• AI vs. Conventional Systems
• Narrow, General, and Super AI
• AI hardware (GPUs, TPUs) and popular frameworks
• AI in the SDLC
• Benefits (automation, predictive insights) and risks (maintenance, data quality)
• AI as a Service (AIaaS) and service contracts

Module 2: Quality Characteristics and Ethics in AI

• Key Quality Factors
• Flexibility, Adaptability, Autonomy
• Transparency & Explainability
• Ethical & Regulatory
• Bias, Reward Hacking, and compliance (e.g., GDPR)
• Risk Management
• Identifying and mitigating biases
• Documentation of AI components

Module 3: Machine Learning Overview

• ML Types
• Supervised, Unsupervised, Reinforcement Learning
• ML Workflow
• Data collection, preprocessing, model training, evaluation, deployment
• Overfitting & Underfitting
• Causes, detection, and mitigation (e.g., regularization)

Lab 1: Overfitting & Underfitting (Titanic Dataset)

• Scenario: Predict passenger survival on the Titanic.
• Goal: Demonstrate how model complexity influences performance.
• Lab Steps:
1. Load and preprocess Titanic data.
2. Train multiple classifiers (e.g., logistic regression vs. random forest).
3. Observe overfitting/underfitting effects on validation accuracy.

Day 2: Data Handling, Regression, and Prompt Engineering

Module 4: Data Preparation & Handling

• Data Quality
• Cleaning, handling missing values, outliers, categorical features
• Train/validation/test splits
• Common Pitfalls
• Imbalanced classes, mislabeled data, domain knowledge gaps

Lab 2: Data Preparation (NYC Taxi Dataset)

• Scenario: Forecast taxi fares in NYC (regression).
• Goal: Clean a real-world dataset and create meaningful features.
• Lab Steps:
1. Load NYC Yellow Cab data (pickup/dropoff times, distances).
2. Handle missing data and detect outliers.
3. Engineer features (e.g., time-of-day, trip distance).

Module 5: Model Evaluation Metrics

• Regression Metrics
• MSE, RMSE, MAE, R²
• Classification Recap
• Accuracy, precision, recall, F1-score, confusion matrix
• Choosing the Right Metric
• Contextual needs (business value, safety-critical)

Lab 3: Regression Modeling (NYC Taxi Fares)

• Scenario: Build and evaluate models to predict fare amounts.
• Goal: Compare linear regression vs. gradient boosting to measure error rates.
• Lab Steps:
1. Train at least two regression models on NYC Taxi data.
2. Compute MSE, RMSE, and MAE on the validation set.
3. Discuss feature importance and next steps.

Module 6: Prompt Engineering for Generative AI

• Prompting Best Practices
• Role-based prompting, zero-shot vs. few-shot, chain-of-thought reasoning
• Structuring prompts for clarity, constraints, and context
• Iterative refinement (synonyms, repeated keywords, output format)

Lab 4: Designing a Sophisticated Prompt for Software Engineering

• Scenario: Generate detailed, actionable advice on software architecture, testing, or refactoring in a microservices environment.
• Goal: Apply advanced prompting techniques (role prompting, constraints, few-shot examples) to create a high-quality prompt that yields expert-level recommendations.
• Lab Steps:
1. Define Context & Role (e.g., “You are a principal software architect…”).
2. Provide Examples (show how you want the answer structured or styled).
3. Add Constraints (limit response length, include specific bullet points).
4. Iterate & Refine (test and adjust wording for clarity & precision).

Day 3: Neural Networks, Explainability, and Responsible AI

Module 7: Neural Networks Introduction

• NN Basics
• Perceptrons, hidden layers, activation functions
• NN Use Cases
• Images, text, speech; large-scale data
• Testing Neural Networks
• Special considerations, coverage measures

Lab 5: Neural Network Classification (MNIST)

• Scenario: Classify handwritten digits from MNIST.
• Goal: Implement and train a feed-forward neural network.
• Lab Steps:
1. Load MNIST images (28x28).
2. Build a simple network (e.g., feed-forward).
3. Evaluate accuracy and discuss improvements (layers, dropout, etc.).

Module 8: Testing & Model Explainability

• Levels of Testing
• Input data testing, model testing, system & acceptance testing
• Adversarial Attacks & Data Poisoning
• Defenses, monitoring strategies
• Explainability Methods
• LIME, SHAP, local vs. global interpretation

Lab 6: Model Explainability (U.S. Housing with LIME)

• Scenario: Stakeholders want insights into house pricing predictions.
• Goal: Use LIME to explain predictions of a regression model.
• Lab Steps:
1. Train a regression model on a U.S. housing dataset (e.g., Ames Housing).
2. Apply LIME to interpret specific predictions.
3. Identify potential biases or anomalies in model behavior.

Module 9: Responsible AI & Wrap-Up

• Governance & Compliance
• Privacy, fairness, disclaimers, accountability
• Future Trends
• Large Language Models (LLMs), multi-modal AI, MLOps
• Key Takeaways
• Data and model versioning, transparency, bias mitigation, robust QA

Summary of Labs

1. Lab 1: Overfitting & Underfitting (Titanic)
2. Lab 2: Data Preparation (NYC Taxi)
3. Lab 3: Regression Modeling (NYC Taxi Fares)
4. Lab 4: Designing a Sophisticated Prompt for Software Engineering (GenAI)
5. Lab 5: Neural Network Classification (MNIST)
6. Lab 6: Model Explainability (U.S. Housing with LIME)