Prompt Engineering & Artificial Intelligence

Week 1: Python for AI Development

• AI-Specific Python Syntax: Mastering list comprehensions, decorators, and generators for efficient data handling.

• Data Handling Libraries: Advanced data manipulation with NumPy for numerical arrays and Pandas for structured datasets.

• API Integration: Writing asynchronous Python code to interact with OpenAI, Anthropic, and Hugging Face APIs using requests and aiohttp.

• Environment Management: Setting up scalable AI environments using Docker, Conda, and .env files for secure API key management.

• Object-Oriented Programming (OOP): Building modular AI scripts and classes to handle reusable model-calling logic.

Week 2: Artificial Intelligence & Machine Learning Foundations

• The AI Landscape: Distinguishing between Symbolic AI, GOFAI, and modern Connectionist approaches.

• Supervised Learning Mechanics: Deep dive into Linear/Logistic Regression, Decision Trees, and Gradient Boosting (XGBoost/CatBoost).

• Statistical Foundation: Understanding Bias-Variance tradeoff, Overfitting/Underfitting, and evaluation metrics like Precision-Recall and AUC-ROC.

• Feature Engineering: Automating feature selection and dimensionality reduction (PCA) to prepare data for ML models.

• The ML Lifecycle: From data collection and labeling to model validation and hyperparameter tuning.

Week 3: Deep Learning & Neural Architectures

• Neural Network Essentials: Building Multi-Layer Perceptrons (MLP); understanding Backpropagation, Activation Functions (ReLU, Softmax), and Optimizers (Adam, SGD).

• Computer Vision (CNNs): Convolutional layers, pooling, and transfer learning for image classification and object detection.

• Sequence Modeling (RNNs to Transformers): Evolution from LSTMs to the Attention Mechanism—the backbone of modern LLMs.

• Framework Mastery: Building and training simple models using PyTorch or TensorFlow/Keras.

• Fine-tuning Foundations: Concepts of “Freezing layers” and adapting pre-trained models for specific niche tasks.

Week 4: Prompt Engineering & LLM Optimization

• Core Prompting Techniques: Mastering Zero-shot, Few-shot, and Chain-of-Thought (CoT) prompting to improve model reasoning.

• Advanced Frameworks: Implementation of Tree-of-Thoughts, ReAct logic, and directional stimulus prompting.

• RAG (Retrieval-Augmented Generation): Connecting LLMs to external data sources using Vector Databases (ChromaDB/Pinecone) and LangChain.

• Prompt Hacking & Defense: Understanding and mitigating Prompt Injection, Jailbreaking, and Hallucinations; implementing output parsers for structured data (JSON/YAML).

• Capstone Project: Building a “Self-Correcting AI Agent” that uses specialized prompts to research, summarize, and validate information autonomously.