Advanced prompting strategies for getting the best results from AI models.
Beyond basic prompt writing lies a rich landscape of techniques that can dramatically improve AI output quality. These strategies β from Chain-of-Thought reasoning to Tree-of-Thought exploration β exploit the way LLMs process and generate text to unlock capabilities that simple prompts cannot.
Mastering prompting techniques is arguably the highest-leverage skill in AI today. The same model can produce mediocre or exceptional results depending entirely on how you prompt it. These techniques work because they shape the model's reasoning process, not just its output format.
Zero-Shot Prompting
Asking the model to perform a task without any examples. Works well for simple, well-defined tasks where the model already has strong capabilities from training.
Few-Shot Prompting
Providing 2-5 input/output examples before your actual request. Shows the model exactly what format, style, and quality you expect β dramatically improves consistency.
Chain-of-Thought (CoT)
Adding "think step by step" or showing reasoning examples forces the model to break problems into steps. Dramatically improves math, logic, and multi-step reasoning accuracy.
Tree-of-Thought
Exploring multiple reasoning paths in parallel, evaluating each, and selecting the best. Like CoT but branching β the model considers several approaches before committing to an answer.
ReAct Pattern
Reasoning + Acting β the model alternates between thinking about what to do and taking actions (tool calls). Powers most AI agents: observe β think β act β observe results β think again.
Role Prompting
"You are an expert in..." activates domain-specific knowledge and communication style. Combining roles with constraints creates powerful persona engineering for consistent outputs.
Constitutional Prompting
Defining principles and rules the model must follow, then having it self-evaluate against those rules. Used by Anthropic for Claude's safety β the model critiques and revises its own outputs.
Prompt Chaining
Breaking a complex task into a sequence of simpler prompts where each output feeds into the next. Enables complex workflows that no single prompt could handle reliably.
Meta-Prompting
Using AI to generate and optimize prompts. Ask the model to write a better version of your prompt, then use that improved prompt. Iterative meta-prompting converges on high-quality prompts.
Structured Output Forcing
Using JSON schemas, XML tags, or markdown templates to constrain output format. Eliminates parsing issues and ensures programmatic usability. Most APIs now support native structured output.
Chain-of-ThoughtPrompting technique that elicits step-by-step reasoning, dramatically improving accuracy on complex tasks.
ReActReasoning + Acting pattern where models alternate between thinking and taking actions with tools.
Few-ShotProviding examples in the prompt to demonstrate desired output format and quality.
Prompt ChainingBreaking complex tasks into sequences of simpler prompts, each building on the previous output.