Duration: 4-5 hours | Difficulty: Intermediate
Module Overview
Now that you understand the fundamentals, it’s time to apply them across different NLP tasks. Each task type has patterns that work exceptionally well. You’ll learn proven templates and techniques for classification, information extraction, generation, transformation, and question-answering tasks.Learning Objectives
By the end of this module, you will be able to:Design effective prompts for text classification tasks
Extract structured information from unstructured text
Generate creative and functional content with constraints
Transform text across styles, formats, and languages
Build robust question-answering systems
Why Task-Specific Patterns Matter
Different tasks require different approaches:- Classification needs constrained outputs
- Extraction benefits from progressive techniques
- Generation requires clear constraints and attributes
- Transformation balances fidelity with adaptation
- QA demands structured reasoning
Module Lessons
Lesson 2.1: Text Classification
Extract reliable classifications from text-generating LLMs
Lesson 2.2: Information Extraction
Pull structured data from unstructured text
Lesson 2.3: Text Generation
Create content with specific constraints and attributes
Lesson 2.4: Text Transformation
Transform text across languages, styles, and formats
Lesson 2.5: Question-Answering
Build robust QA systems with structured reasoning
What You’ll Build
Throughout this module, you’ll work on practical applications:- Sentiment classifier for customer reviews
- Entity extractor for business documents
- Content generator for marketing copy
- Style transformer for different audiences
- QA system for technical documentation
Task-Pattern Quick Reference
| Task | Best Pattern | Key Technique |
|---|---|---|
| Classification | Constrained output | Explicit label set |
| NER | Progressive extraction | Simple → Complex |
| Generation | Attribute specification | Constraints + examples |
| Transformation | Source-target clarity | Balance fidelity & adaptation |
| QA | Reasoning structure | Step-by-step breakdown |
Prerequisites
Before starting this module, ensure you’ve completed:- ✅ Module 1: Foundations of Prompting
- ✅ Understanding of the four core principles
- ✅ Familiarity with in-context learning
Module Assessment
After completing all lessons, you’ll tackle a capstone project: building a complete content moderation system that:- Classifies content safety levels
- Extracts problematic elements
- Generates explanations
- Suggests modifications
Estimated Time: Each lesson takes 45-60 minutes. Plan for 4-5 hours total, plus assessment time.
Ready to Begin?
Start Lesson 2.1: Text Classification Prompts
Learn how to extract reliable classifications from LLMs