Prompting Strategies

Interleave reasoning traces with task-specific actions.

Decompose questions by asking and answering follow-up questions.

Connect multiple focused prompts in sequential workflows.

Assign an expert persona to access specialized knowledge.

Add emotional context to enhance AI response quality and engagement.

Control writing style, tone, and voice characteristics.

Let AI ask clarifying questions before responding.

Regenerate context to remove irrelevant information before answering.

Simulate different perspectives for theory-of-mind reasoning.

Rephrase the question before answering to improve comprehension.

Re-read the question to enhance understanding of complex problems.

Define AI behavior, personality, and constraints through persistent system-level instructions.

Ground AI responses in retrieved documents to reduce hallucination and deliver source-backed answers.

Design AI systems that autonomously plan, execute, and adapt tasks using tools and reasoning loops.

High-level structural templates that teach HOW to approach tasks rather than providing specific content.

Process multiple task instances in a single prompt for efficiency and cost reduction.

Simply repeating key instructions improves non-reasoning LLM performance through emphasis.

Use targeted hints and cues to steer LLM outputs toward desired directions.

Model generates relevant knowledge before answering to improve accuracy on reasoning tasks.

Generate hypothetical answer documents to improve retrieval without task-specific training.

Transform task inputs into question-answer format and aggregate multiple reformulated responses.

Use reflective Socratic questioning to guide AI through iterative reasoning and discovery.

Use multi-turn dialogue structure to guide reasoning through conversational exchange.

Infer task instructions from a few input-output demonstrations automatically.

Automatically generate and select optimal prompt instructions via LLM-driven search.

Use LLMs as optimizers where the optimization task is described in natural language.

Generate multiple semantically similar prompt variants and ensemble their responses.

Treat reasoning as planning using the LLM as both world model and agent with MCTS.

Synergize reasoning, acting, and planning via Monte Carlo Tree Search for agents.

Iteratively refine summaries to be progressively more entity-dense without increasing length.

Step-by-step templates for clear, repeatable prompt structure.

Teach AI by demonstration with zero-shot, few-shot, and example selection.

Chain-of-thought methods for transparent, step-by-step reasoning.