Enables the AI to respond directly to instructions using its built-in knowledge without examples.
Uses a single example to illustrate the expected format for the AI to follow.
Directs the AI to articulate each reasoning step sequentially to solve complex problems.
Expands reasoning into branching paths, allowing the AI to explore and evaluate multiple possibilities.
Alternates between reasoning and taking actions, allowing the AI to observe outcomes and adjust.
Supplies relevant background information within the prompt to produce informed responses.
Incorporates affective cues to shape the AI's output, evoking or responding to feelings.
Assigns a particular persona to the AI, directing it to respond from that viewpoint.
Offers a small set of examples to illustrate the desired pattern for the AI to generalize from.
Generates multiple responses and selects the most consistent one to increase reliability.
Directs the AI to create or optimize prompts for subsequent use, fostering self-reflection.
Starts with the simplest aspects of a problem and progressively tackles more complex parts.
Bundles several related activities into one instruction for concurrent execution.
Dissects a major objective into smaller, sequential components for systematic resolution.
Sets explicit boundaries such as length or format to direct the AI's output.
Has the AI first produce pertinent facts before addressing the main query to enrich the response.
Tasks the AI with automatically crafting or enhancing prompts to streamline optimization.
Supplies guiding hints or emphases to steer the AI's focus toward particular aspects.
A prompting style where the model generates an answer and then verifies its reasoning step-by-step to catch errors and ensure accuracy.
The model creates an outline or 'skeleton' of the response first, then fills in the details for better structure and coherence.
Explores multiple connected reasoning paths like a web, merging ideas for richer, non-linear problem-solving.
Splits tasks into a planning phase first, followed by execution, with optional self-checks for enhanced accuracy.
Employs Socratic questioning to clarify ambiguities and elicit precise requirements before finalizing responses.
The AI iteratively reviews and refines its output through self-critique loops to elevate quality.
Progressively compresses information into denser summaries while retaining essential entities and facts.
Dynamically adapts prompt strategies based on interim feedback or scoring for optimized performance.
Integrates external retrieved knowledge into prompts to ground responses in up-to-date, factual data.
Simulates debate among multiple AI personas to critically refine and converge on consensus answers.
Fluidly shifts between AI personas or roles within a interaction for multifaceted perspectives.
Deconstructs complex tasks into incremental, supported steps that build cumulatively.
Executes a rapid initial generation followed by a reflective second pass for refinement.
Dynamically adjusts prompt context volume—expanding for depth or compressing for focus—within token limits.
Structures prompts around explicit objectives, with progress tracking for directed, measurable execution.