These advanced techniques go beyond templates to help you manage complexity, improve reliability, and enhance the reasoning power of large language models. They are especially useful in professional, technical, and multi-step tasks.
Encourages the model to show its reasoning by asking it to think step-by-step. Ideal for math, logic, and structured problem-solving.
Refines a response in stages based on feedback or follow-up criteria. Useful for polishing drafts, improving accuracy, or aligning with specific goals.
Generates multiple reasoning paths and selects the most common answer, reducing hallucination risk in factual or analytical tasks.
Tests model robustness by intentionally challenging assumptions or injecting counter-arguments. Helps ensure high-integrity outputs.
Build abstract task representations from multiple prompts to apply them across similar challenges. Useful in research and experimentation.
Switches between multiple roles or perspectives in a single prompt sequence for layered output (e.g., ideation + critique + final polish).
Instructs the model to evaluate or rank its own output for quality or accuracy. Can help guide iterative improvement loops.
Instructs the model to cite sources or pull from specific materials (like uploaded docs or URLs), aligning results with facts.