Your Online Developer Toolbox

Learn advanced AI prompt engineering techniques, including structure, context control, formatting patterns, and optimization strategies to improve output reliability.

Learn how Chain-of-Thought prompting works, why it improves reasoning, and how to use step-by-step thinking patterns to generate more accurate and reliable AI outputs.

Learn how Tree-of-Thought prompting enables deliberate multi-path reasoning, exploration, and problem-solving for complex tasks using LLMs.

Learn how Reflexion AI prompting helps LLM agents self-correct, store lessons, and improve over repeated attempts using feedback-driven reflections.

Learn how Graph-of-Thought prompting enables LLMs to reason using graph structures—connecting ideas, branching paths, and parallel reasoning nodes for deeper problem-solving.

Learn how shot prompting helps LLMs perform better by using zero-shot, one-shot, or few-shot examples. Master example-driven prompting to improve accuracy, consistency, and task generalization.

Learn Least-to-Most prompting — a powerful technique where LLMs break complex tasks into smaller steps before solving the final problem. Improve reasoning accuracy and reduce errors.

Learn React Prompting — an iterative prompting technique where LLMs critique, review, and refine their own answers to improve quality, accuracy, and reasoning.