COURSE OUTLINE

Day 1: Foundations of NLP and the Generative Shift

Focus: From linguistics to Large Language Models.

• NLP Fundamentals: Text normalization, tokenization, and part-of-speech tagging in the modern era.
• The Evolution of Language Models: From N-grams to Word2Vec, and the breakthrough of the Transformer architecture.
• Generative AI Concepts: Understanding probability distributions in text and how Gemini "predicts" the next token.
• Hands-on: Setting up a Python environment and performing basic text analysis with the Gemini API.

Day 2: Advanced Prompt Engineering for NLP Tasks

Focus: Directing LLMs to perform complex linguistic work.

• The Prompting Hierarchy: Zero-shot vs. Few-shot learning for specialized NLP tasks.
• Structured Information Extraction: Using Gemini to extract JSON-formatted data from messy, unstructured text.
• Text Transformation: Automated translation, style transfer (formal to casual), and long-form summarization.
• Hands-on Lab: Building an automated "Email Intelligence" tool that categorizes, summarizes, and extracts action items from threads.

Day 3: Embeddings and Vector Databases

Focus: Mapping language to mathematical space.

• Understanding Embeddings: How text is converted into high-dimensional vectors.
• Semantic Similarity: Using Cosine Similarity to find related documents or concepts.
• Vector Search Infrastructure: Introduction to Vertex AI Vector Search and local alternatives like FAISS.
• Hands-on Lab: Creating a semantic recommendation engine that finds similar news articles based on meaning rather than tags.

Day 4: Retrieval-Augmented Generation (RAG)

Focus: Connecting AI to private knowledge bases.

• The RAG Architecture: Orchestrating the flow between User Query → Vector Search → LLM Context.
• Document Processing: Advanced chunking strategies and handling different file types (PDF, Markdown, HTML).
• Grounding and Verification: Reducing hallucinations by forcing Gemini to cite its sources.
• Hands-on Lab: Developing a "Corporate Wiki Assistant" that answers HR and Policy questions using internal company documents.

Day 5: Fine-Tuning and Domain Adaptation

Focus: Specialized models for specialized industries.

• When to Fine-Tune: Evaluating the trade-offs between RAG and Parameter-Efficient Fine-Tuning (PEFT).
• Dataset Preparation: Cleaning and formatting domain-specific data for model training.
• Evaluation Metrics: Understanding BLEU, ROUGE, and using "LLM-as-a-Judge" for qualitative assessment.
• Hands-on Lab: Fine-tuning a smaller model (Gemma) for a specific medical or legal terminology task.

Day 6: Deployment, Ethics, and Governance

Focus: Shipping reliable and responsible AI.

• AI Security: Protecting against prompt injection and ensuring data privacy on Google Cloud.
• Responsible AI Frameworks: Implementing Google’s safety filters and bias detection.
• Production Workflows: Using Vertex AI Pipelines to monitor model performance and data drift.
• Final Project: Building a complete, end-to-end "Insight Engine" that ingests real-time data and provides AI-driven analysis.