AI 212 Programming with AI Tools

1. Develop software solutions that integrate AI models or services using programming languages, frameworks, or APIs.

2. Apply machine-learning and automation concepts to solve practical, real-world problems.

3. Implement effective prompt-engineering techniques within programming contexts to improve AI system performance and output quality.

4. Handle and preprocess data programmatically for use in AI or machine-learning workflows.

5. Evaluate and debug AI-enhanced programs to ensure accuracy, reliability, and appropriate system behavior.

6. Incorporate ethical, legal, and security considerations into the design and deployment of AI-driven applications.

7. Design and complete a hands-on project demonstrating the integration of multiple AI tools or services to meet a defined objective.

• Introduction to AI Programming

  • Review of AI principles and system components

  • The role of programming in applied AI

  • Overview of AI frameworks, APIs, and cloud-based services

  • Setting up the development environment (Python, libraries, or equivalent tools)

• Working with AI Services and APIs

  • Understanding AI APIs (e.g., OpenAI, Google, Hugging Face, Azure Cognitive Services)

  • Making API calls, sending/receiving data, and parsing results

  • Authentication, rate limits, and responsible use of AI services

  • Integrating APIs into existing codebases or applications

• Prompt Engineering for Programmers

  • Writing effective programmatic prompts and dynamic queries

  • Managing context, tokens, and parameters for consistent results

  • Automating and chaining prompts through code

  • Using prompt templates for text, image, or data generation

• Data Handling and Preprocessing

  • Data collection and cleaning for AI workflows

  • Structuring datasets for training and inference

  • Using libraries for data manipulation (e.g., Pandas, NumPy, JSON)

  • Handling unstructured vs. structured data

• Machine Learning and Model Integration

  • Overview of machine-learning models and APIs

  • Using pre-trained models for NLP, vision, and analytics tasks

  • Fine-tuning vs. using pre-built models

  • Evaluating model accuracy and reliability

• Automation and Workflow Design

  • Building scripts that automate AI tasks (summarization, classification, report generation)

  • Combining multiple AI services for multi-step processes

  • Scheduling, input/output management, and pipeline design

  • Practical examples: chatbots, document analyzers, data enrichment

• Debugging and Performance Evaluation

  • Common issues in AI-enhanced programs (timeouts, token limits, bad data)

  • Testing and validation techniques

  • Logging and monitoring AI interactions

  • Assessing performance, cost, and efficiency

• Ethics, Security, and Responsible AI Development

  • Addressing data privacy and compliance in AI applications

  • Avoiding misuse and model overreliance

  • Transparency, explainability, and auditability

  • Incorporating fairness and bias mitigation in design choices

• Capstone Project: Applied AI Application

  • Design and implement a functional AI-driven application or system

  • Integrate at least one API or model with a user interface or workflow

  • Document design, implementation, and ethical considerations

  • Present and demonstrate final project outcomes

• Reflection and Future Trends

  • Review of tools and frameworks used

  • Discussion of scalability, maintenance, and future-proofing AI systems

  • Emerging areas: multimodal models, agents, and automation frameworks

  • Student reflection on growth and readiness for applied AI work