A sophisticated problem-solving approach where the LLM explores multiple solution paths simultaneously. It creates a decision tree of possibilities, evaluating each branch's potential before choosing the optimal path.
Complex ProblemsMulti-step Solutions
⛓️ Chain of Thought
Guide the LLM through explicit step-by-step reasoning, breaking down complex problems into smaller, manageable pieces. This helps in maintaining logical consistency and catching potential errors early.
Step-by-stepLogical Flow
🎯 Zero-Shot CoT
Enable the LLM to tackle new problems without specific examples by prompting it to "think step by step" or "let's approach this systematically." This method leverages the model's inherent reasoning capabilities.
No Examples NeededVersatile
🛠️ Practical Techniques
📝 Clear Context Setting
Provide comprehensive context and specific requirements upfront. Include relevant background information, desired format, and any constraints or preferences that should guide the response.
Background InfoRequirements
🔄 Iterative Refinement
Use a feedback loop to continuously improve responses. Start with a basic prompt, evaluate the output, and refine your instructions based on what worked and what didn't.
Feedback LoopProgressive Improvement
🎭 Role Assignment
Assign specific roles or expertise levels to the LLM to frame its responses appropriately. This helps in getting responses tailored to particular perspectives or knowledge domains.
Expert ModePerspective Shift
💡 Practical Examples
Basic: "Write code for a website" Enhanced: "Create a responsive HTML/CSS layout for a portfolio website, optimized for mobile devices, with a dark theme and smooth animations"
Basic: "Explain machine learning" Enhanced: "Explain how machine learning algorithms learn from data, using analogies suitable for a high school student, focusing on practical applications in smartphone technology"
Basic: "Help debug my code" Enhanced: "Review this Python function for potential edge cases, performance issues, and security vulnerabilities, suggesting improvements using current best practices"
