Duration: 5-6 hours | Difficulty: Advanced
Module Overview
You’ve mastered the fundamentals and task-specific patterns. Now it’s time to level up with advanced techniques that unlock sophisticated reasoning capabilities. These methods—Chain of Thought, problem decomposition, self-refinement, and RAG—are what separate basic prompting from expert-level AI orchestration.Learning Objectives
By the end of this module, you will be able to:Apply Chain of Thought prompting to improve reasoning accuracy
Decompose complex problems into manageable sub-problems
Implement self-refinement and iterative improvement strategies
Use ensembling and multi-path reasoning for robust solutions
Integrate external tools and knowledge through RAG
Why Advanced Techniques Matter
Complex Reasoning
Standard prompting struggles with multi-step logic and abstract reasoning
Accuracy Gains
Advanced techniques can improve accuracy by 30-50% on reasoning tasks
Transparency
Make the AI’s reasoning process visible and verifiable
Reliability
Reduce errors and hallucinations through systematic approaches
The Advanced Techniques Landscape
Module Lessons
Lesson 3.1: Chain of Thought
Unlock step-by-step reasoning with CoT prompting
Lesson 3.2: Problem Decomposition
Break complex problems into manageable pieces
Lesson 3.3: Self-Refinement
Iteratively improve outputs through self-critique
Lesson 3.4: Ensembling
Combine multiple reasoning paths for robustness
Lesson 3.5: Tool Integration & RAG
Connect LLMs to external knowledge and tools
Technique Comparison
| Technique | Best For | Accuracy Gain | Complexity |
|---|---|---|---|
| Chain of Thought | Multi-step reasoning | +30-40% | Medium |
| Decomposition | Complex, hierarchical problems | +40-50% | High |
| Self-Refinement | Quality-critical outputs | +20-30% | Medium |
| Ensembling | High-stakes decisions | +25-35% | High |
| RAG | Knowledge-intensive tasks | +50-60% | High |
Real-World Applications
Throughout this module, you’ll build systems for:- Mathematical Problem Solver - Multi-step calculations with verification
- Research Assistant - Complex question answering with source citation
- Code Debugger - Systematic error identification and fixing
- Strategic Planner - Breaking down business problems into actionable steps
- Knowledge-Grounded Chatbot - Accurate responses backed by external sources
The Research Foundation
These techniques are backed by cutting-edge research:
- Chain of Thought: Wei et al. (2022) - “Chain-of-Thought Prompting Elicits Reasoning in Large Language Models”
- Least-to-Most: Zhou et al. (2022) - “Least-to-Most Prompting Enables Complex Reasoning”
- Self-Consistency: Wang et al. (2022) - “Self-Consistency Improves Chain of Thought Reasoning”
- RAG: Lewis et al. (2020) - “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks”
Prerequisites
Before starting this module, ensure you’ve completed:- ✅ Module 1: Foundations of Prompting
- ✅ Module 2: Task-Specific Prompting Patterns
- ✅ Understanding of few-shot learning
- ✅ Familiarity with structured output formats
What Makes These Techniques “Advanced”?
1. Multi-Step Reasoning
1. Multi-Step Reasoning
Unlike basic prompting, these techniques explicitly model the reasoning process, making each step transparent and verifiable.
2. Error Correction
2. Error Correction
Advanced techniques include mechanisms for catching and correcting errors, either through self-critique or multiple reasoning paths.
3. External Integration
3. External Integration
They go beyond the model’s internal knowledge by connecting to external tools, databases, and knowledge sources.
4. Systematic Approaches
4. Systematic Approaches
Rather than hoping for good outputs, these techniques provide systematic frameworks that consistently produce high-quality results.
Performance Expectations
Important: Advanced techniques require more tokens and processing time. Use them when:
- Accuracy is critical
- Problems are genuinely complex
- Standard prompting has failed
- Transparency is required
- Stakes are high
Module Assessment
After completing all lessons, you’ll build a Multi-Capability AI Assistant that:- Uses Chain of Thought for reasoning
- Decomposes complex queries
- Self-refines its outputs
- Grounds answers in external knowledge (RAG)
- Provides transparent, verifiable responses
Estimated Time: Each lesson takes 60-75 minutes. Plan for 5-6 hours total, plus assessment time.
Success Metrics
You’ll know you’ve mastered advanced prompting when you can:Reason Transparently
Make the AI’s thinking process visible and verifiable
Handle Complexity
Break down and solve multi-step, hierarchical problems
Ensure Accuracy
Achieve consistent, reliable results on challenging tasks
Integrate Knowledge
Connect LLMs to external information sources effectively
Ready to Begin?
These advanced techniques will transform how you work with LLMs. Let’s start with the breakthrough that started it all: Chain of Thought prompting.Start Lesson 3.1: Chain of Thought Prompting
Discover how “Let’s think step-by-step” revolutionized AI reasoning