ReAct Prompting: Combining Reasoning and Actions
ReAct prompting connects an AI model’s decisions to real tool results, making multi-step tasks more grounded, inspectable and controllable.
Topic
Prompting
How to turn a vague request into a clear task, supply context and constraints, and judge whether an answer is actually useful.
Prompting for Structured Output: JSON, Tables and Schemas
Learn how to prompt AI for dependable JSON, tables and schema-based responses, then validate the results before they enter your workflow.
Few-Shot Prompting: Designing Examples That Teach the Model
Few-shot prompting works best when examples reveal the decisions, boundaries and output conventions that instructions alone leave unclear.
System Prompts and Role Prompting: What They Change and What They Don't
System prompts and roles can shape an AI assistant’s behaviour, but reliable results depend on clear tasks, relevant evidence, sensible boundaries and testing.
Self-Consistency and Verification Prompts for More Reliable Answers
Self-consistency and verification prompts can make AI answers more dependable when you separate agreement from evidence and test the claims that matter.
Chain-of-Thought Prompting: When Step-by-Step Reasoning Helps
Chain-of-thought prompting can help AI tackle multi-step problems, but its real value comes from useful decomposition, visible checks and knowing when a simpler prompt is better.
Tree of Thoughts and Advanced Reasoning Prompt Patterns
Tree of Thoughts turns difficult AI tasks into a controlled search through alternatives, with explicit checks, pruning and stopping rules.
Prompt Chaining: Breaking Complex Tasks Into Reliable Steps
Prompt chaining turns a large AI request into smaller, checkable stages, making complex work easier to inspect, correct and repeat.
Evolutionary Prompt Optimization: The Complete Guide to Promptbreeder & APE
How Promptbreeder and APE use evolutionary algorithms to mutate and self-improve LLM prompts.