> ## Documentation Index > Fetch the complete documentation index at: https://prompt.university/llms.txt > Use this file to discover all available pages before exploring further. # Module 3: Advanced Prompting Techniques > Master Chain of Thought, problem decomposition, self-refinement, and RAG for complex reasoning tasks **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 Standard prompting struggles with multi-step logic and abstract reasoning Advanced techniques can improve accuracy by 30-50% on reasoning tasks Make the AI's reasoning process visible and verifiable Reduce errors and hallucinations through systematic approaches ## The Advanced Techniques Landscape ```mermaid theme={null} graph TD A[Complex Problem] --> B[Chain of Thought] A --> C[Problem Decomposition] B --> D[Self-Refinement] C --> D D --> E[Ensembling] E --> F[Tool Integration & RAG] F --> G[Robust Solution] ``` ## Module Lessons Unlock step-by-step reasoning with CoT prompting Break complex problems into manageable pieces Iteratively improve outputs through self-critique Combine multiple reasoning paths for robustness 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"? Unlike basic prompting, these techniques explicitly model the reasoning process, making each step transparent and verifiable. Advanced techniques include mechanisms for catching and correcting errors, either through self-critique or multiple reasoning paths. They go beyond the model's internal knowledge by connecting to external tools, databases, and knowledge sources. 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: Make the AI's thinking process visible and verifiable Break down and solve multi-step, hierarchical problems Achieve consistent, reliable results on challenging tasks 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. Discover how "Let's think step-by-step" revolutionized AI reasoning