An AI Prompting Technique is a method of crafting input instructions or queries to guide an artificial intelligence model to produce a desired output. It involves structuring the prompt—whether through specific wording, context, examples, or constraints—to elicit accurate, relevant, or creative responses.

Below are the 18 well-known Prompting Techniques listed by category.

Basic Prompting

Techniques that use simple instructions with minimal setup for straightforward tasks.

• Zero-Shot Prompting: The AI completes a task based solely on a single instruction without any examples.
• Few-Shot Prompting: The AI learns a task pattern by following a few provided examples to mimic the desired format.

Reasoning-Based

Techniques that encourage the AI to think logically or explore multiple solutions to solve complex problems.

• Chain-of-Thought (CoT) Prompting: The AI explains its reasoning step-by-step to solve a problem accurately.
• Tree of Thoughts (ToT) Prompting: The AI explores multiple possible solutions simultaneously to find the best answer.
• ReAct Prompting: The AI alternates between reasoning about a step and taking an action, like searching, to solve a problem.
• Multimodal CoT Prompting: The AI reasons step-by-step using both text and images to address a problem.

Tool-Augmented

Techniques that leverage external tools or data sources to enhance the AI’s responses.

• Retrieval Augmented Generation (RAG) Prompting: The AI uses up-to-date information from external sources, like a database or the web, to answer a question.
• Automatic Reasoning and Tool-use (ART) Prompting: The AI selects and uses tools, like calculators or web searches, to answer accurately.
• Program-Aided Language Models (PAL) Prompting: The AI writes and runs code to solve a problem and uses the result to answer.

Self-Improving

Techniques that enable the AI to refine its own outputs or prompts for better performance.

• Self-Consistency Prompting: The AI answers the same question multiple times and selects the most common response for accuracy.
• Automatic Prompt Engineer (APE) Prompting: The AI creates and tests different prompts to find the most effective one for a task.
• Reflexion Prompting: The AI reviews its own answer, identifies errors, and improves it in the next attempt.

Advanced Structuring

Techniques that use structured approaches or human interaction to guide complex tasks.

• Meta Prompting: The AI, acting as an expert, designs a detailed prompt to guide itself or another AI in a task.
• Generate Knowledge Prompting: The AI gathers and lists relevant facts about a topic before providing a final answer.
• Prompt Chaining Prompting: A complex task is broken into a series of smaller, connected prompts to guide the AI step-by-step.
• Active-Prompt Prompting: The AI flags unclear cases and asks a human for clarification to improve its response.
• Directional Stimulus Prompting: The prompt includes hints or keywords to guide the AI toward a specific type of response.
• Graph Prompting: The AI uses a graph to map relationships between items and answer questions about their connections.

Why Are Prompting Techniques Needed?

Prompting techniques are necessary because AI models, like me, rely on input prompts to understand user intent and generate relevant responses. Here’s why they matter:

1. Clarity and Specificity: AI models interpret prompts literally. Vague or ambiguous prompts can lead to off-target or generic outputs. Techniques like providing context or examples help focus the response.
2. Task Complexity: Some tasks require step-by-step reasoning, specific formats, or creative outputs. Techniques like chain-of-thought or role-based prompting guide the AI to process complex requests systematically.
3. Model Limitations: AI doesn’t inherently “know” user expectations. Prompting techniques bridge this gap by structuring inputs to align with the model’s training, reducing errors or irrelevant answers.
4. Customization: Different tasks (e.g., translation, summarization, coding) need tailored approaches. Techniques like few-shot prompting provide examples to set the tone, style, or format.
5. Efficiency: Well-crafted prompts save time by reducing the need for follow-up clarifications or iterations.