Move beyond guesswork. Learn how to use A/B testing and quantitative metrics to scientifically prove which prompt variations are most effective, ensuring your applications are built on a foundation of data.
Harness the power of debate. Learn to use Adversarial Self-Critique to create two AI personas—a proposer and a critic—and have them engage in a structured dialogue to refine ideas and produce incredibly robust outputs.
Unlock the reasoning capabilities of LLMs by prompting them to 'think step-by-step.' Learn the fundamentals of Chain-of-Thought (CoT) prompting, a transformative technique for solving complex problems.
A comfortable mind is a lazy mind. Learn how to use Cognitive Dissonance Induction to present an LLM with conflicting information, forcing it to grapple with uncertainty and engage in a deeper, more creative level of analysis.
Prompt your LLM to think at the right altitude. Learn how Conditional Abstraction Scaling allows a model to dynamically adjust its level of reasoning, from high-level strategy to low-level detail, based on the context of the problem.
Harness the two fundamental modes of human thought. Learn to prompt an LLM for both broad, creative 'divergent' thinking and focused, logical 'convergent' thinking to build a complete problem-solving engine.
Explore the speculative frontier of prompt engineering. Learn about the concept of Hyperdimensional Pattern Matching and how it might be used to prompt LLMs for novel, cross-domain insights and creative analogies.
What happens when an LLM contradicts itself? Learn how to use Iterative Contradiction Resolution to prompt a model to identify, analyze, and resolve inconsistencies in its own knowledge and reasoning.
Go beyond simple reasoning and teach your LLM to be aware of its own thought processes. Discover how Meta-Cognition Prompting can unlock a new level of self-awareness, confidence estimation, and strategic thinking in your models.
Why reason in English when you can reason in Python? Learn how Program-Aided Language Models (PAL) offload complex reasoning and calculation to a code interpreter, leading to more accurate and reliable results.
Unlock advanced capabilities by breaking down complex tasks into a series of smaller, interconnected prompts. Learn the art of prompt chaining to build powerful, multi-step reasoning workflows.
Bridge the gap between thought and action. Learn how the ReAct framework enables LLMs to not just reason about a problem, but to actively interact with external tools and environments to gather information and execute tasks.
Go deeper when you need to. Learn how Recursive Thought Expansion (RTE) allows you to dynamically control the depth and detail of an LLM's reasoning, creating a flexible and adaptive problem-solving process.
How does an AI agent learn from its mistakes? Discover the Reflexion framework, which enables agents to reflect on their past actions, generate verbal reinforcement, and improve their performance over time.
Go beyond a single chain of thought. Learn how Self-Consistency, a powerful technique that generates multiple reasoning paths and picks the best one, can dramatically improve the accuracy and reliability of your LLM's answers.
Don't let your LLM be overconfident. Learn how to use Self-Skepticism Reinforcement to prompt your model to challenge its own assumptions, consider alternatives, and build true critical thinking skills.
Harness the power of a 'team of rivals' within a single LLM. Learn how Simulated Multi-Agent Debate (SMAD) can generate highly robust and nuanced outputs by prompting an AI to play multiple, conflicting roles.
Improve the quality and trustworthiness of your LLM's reasoning by prompting it to justify its decisions. Learn how Step-by-Step Rationalization (STaR) can lead to better, more transparent, and self-correcting thought processes.
Bridge the gap between neural and symbolic AI. Learn how to integrate the power of LLMs with the rigor of formal logic, enabling a new class of applications that are both creative and demonstrably correct.
Understand the critical difference between system and user messages in conversational AI and learn how to use them effectively to create robust, reliable, and stateful interactions.
LLMs live in an eternal present. Learn how Temporal Context Augmentation can provide them with a sense of time, enabling them to reason about sequences, causality, and the evolution of events.
Your first prompt is rarely your best. Learn the systematic process of iterating on and refining your prompts to achieve optimal performance, reliability, and quality in your LLM applications.
Learn how to structure prompts with formatting and delimiters to improve clarity, control, and performance in large language models.
Move beyond linear reasoning. Discover Tree of Thoughts (ToT), an advanced prompting framework that enables LLMs to explore, evaluate, and backtrack through multiple reasoning paths, unlocking solutions to complex planning and search problems.
Create something truly new. Learn how Zero-Shot Concept Fusion can prompt an LLM to blend two or more disparate concepts into a single, novel idea, unlocking a powerful engine for innovation.