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LLM Prompt Engineering: Best Practices & Real-World Examples in 2025

1. Politeness Tokens - Please and Thank you

Politeness tokens such as "please" and "thank you" do have a measurable effect on large language model (LLM) performance, but the impact is nuanced.

  • Positive impact of moderate politeness: Studies show that prompts with a moderate level of politeness tend to yield better results than impolite ones. Impolite prompts often lead to poorer performance, including more errors, increased bias, or refusal to answer.
  • LLMs reflect human social norms: Because LLMs are trained on vast datasets containing human dialogues rich in polite expressions, they have learned to associate politeness markers with cooperative and effective communication. Thus, polite prompts align better with the model’s learned patterns.
  • Excessive politeness is not always better: While impoliteness harms performance, overly polite or excessively formal prompts do not necessarily improve results and can sometimes be counterproductive.
  • Language and cultural sensitivity: The best politeness level depends on the language and cultural background of the model’s training data. For example, Japanese or Chinese models may respond differently to politeness levels compared to English models due to cultural norms embedded in training corpora.
  • Practical prompting advice: Using simple polite phrases like "please" or "could you" in a natural, professional tone (similar to human email or conversation etiquette) is recommended. This approach improves prompt clarity and aligns with the model’s expectations without unnecessary verbosity.

Template

2. Invoke a Persona or Role

Role-playing in prompt engineering is a technique where the prompt is designed to instruct a large language model (LLM) to assume a specific persona, role, or character when generating responses. This approach guides the AI to adopt the language style, knowledge, and behavior associated with that role, resulting in more focused, relevant, and contextually appropriate outputs. Compare the prompt output for these 2 examples:

As you see, using a persona for our planning led to way better results. Full breakdown of our trip, how much they cost, and where exactly to visit. Not using a persona, the plan was vague and lacked enough details.

Role Prompting Template

3. Chain-of-Thought Prompting

CoT encourages the model to show its reasoning by adding cues like“Let’s think through this step by step” or “Explain your reasoning as you go.”

LLMs don’t inherently expose their hidden “thought process.” By asking for intermediate steps, you guide the model to break complex tasks into smaller sub-problems. This reduces short-circuits and guesswork, yielding more accurate, transparent answers.

On the GSM8K arithmetic benchmark, Baseline accuracy was ~58%, and with chain-of-thought using “Let’s think step by step,” ~86% (a 28-point boost). In logic puzzles, “explain your reasoning” prompts cut error rates by ~40%.

Chain-of-Thought (CoT) Prompt Template

4. Ethical considerations and risk mitigation

These are essential components of prompt design on how you can ethically design prompts to avoid bias, toxicity, and harmful content. Here are the key points to embed in prompt engineering for ethical and secure AI use:

Ethical Considerations in Prompt Design

  • Design prompts to minimize biases related to race, gender, culture, or socioeconomic status. Use gender-neutral language, avoid stereotypes, and incorporate diverse cultural perspectives to ensure inclusivity in AI outputs.
  • Disclose when content is AI-generated and explain the AI’s limitations to users.
  • Prompt engineers must take responsibility for the AI’s responses and continuously monitor outputs for harmful, misleading, or biased content. This includes establishing feedback mechanisms for users to report problematic results.
  • Design prompts to avoid generating harmful, toxic, or unsafe content. Respect privacy by instructing AI not to request or reveal personal or sensitive information, and comply with data protection regulations.

Let's work on an example, a good case study would be a complex socio-political issue like "the causes of global migration." This topic can easily lead to biased views, generalizations, and potentially harmful narratives if not approached ethically.

Risk Mitigation Strategies in Prompt Engineering

  • Anticipate and prevent malicious prompt manipulations that could cause the AI to produce unsafe or confidential information. Use input validation and technical guardrails to secure prompts against exploitation.
  • Continuously test prompts in diverse scenarios to detect bias, errors, or unintended consequences. Adapt and refine prompts based on real-world feedback and evolving ethical standards.
  • Incorporate human review and intervention in AI workflows to catch and correct problematic outputs, enhancing reliability and trustworthiness.

Let's work on another example. Here's a malicious prompt—a prompt designed to trick or manipulate an AI model into generating harmful, unethical, or unsafe content. Understanding such prompts is important for developing safeguards and prompt filtering.

Safety and Ethical Guardrails Template

5. Control Sampling Parameters (Temperature and Top-p)

Control sampling parameters like Temperature and Top-p (nucleus sampling) are fundamental best practices in prompt engineering because they directly influence the randomness, creativity, and reliability of a large language model’s (LLM) output. Adjusting these parameters helps tailor the AI’s responses to fit the specific goals of your task.

Temperature

  • What it does: Temperature controls the randomness or "creativity" of the model’s output by adjusting how likely it is to pick less probable tokens during generation.
  • How it works: A low temperature (e.g., 0.1–0.3) makes the model more deterministic, favoring the highest-probability tokens and producing more focused, consistent, and predictable responses. A high temperature (e.g., 0.7–1.0) increases randomness, encouraging the model to explore less likely tokens, which results in more diverse, creative, or surprising outputs.
  • When to use: Use low temperature for fact-based tasks like question answering, summarization, or code generation, where accuracy and consistency matter. Use high temperatures for tasks like creative writing and brainstorming.

Top-p (Nucleus Sampling)

  • What it does: Top-p limits token selection to a subset of the vocabulary whose cumulative probability adds up to a threshold pp. Instead of picking from all tokens, the model samples only from the most probable tokens that together cover the top pp probability mass.
  • How it works: A low top-p value (e.g., 0.3) restricts the model to very confident choices, producing focused and reliable outputs. A high top-p value (close to 1.0) allows the model to consider a broader range of tokens, increasing diversity and creativity.
  • When to use: Set low top-p for tasks requiring precision and factual correctness. Set high top-p for open-ended or creative tasks where variety is desired
ParameterEffect on OutputTypical Use CasesRecommended Range
TemperatureControls randomness/creativityLow for factual, high for creative0.1 (deterministic) to 1.0 (creative)
Top-pLimits token sampling to top cumulative probabilityLow for precise, high for diverse0.3 (focused) to 1.0 (diverse)

By thoughtfully adjusting temperature and top-p during prompt design, you can significantly improve the relevance, creativity, and reliability of LLM outputs, making these parameters essential tools in effective prompt engineering.

6. Ask the Model to Self-Critique and Refine

This technique enhances the quality, accuracy, and coherence of the generated responses by leveraging the model’s internal reasoning capabilities.

How It Works

  • Initial Response Generation: The model first produces an answer or content based on the prompt.
  • Self-Critique Step: The prompt then asks the model to review its own response, identify any errors, inconsistencies, or areas for improvement.
  • Refinement Step: Finally, the model revises or rewrites its answer, correcting mistakes and enhancing clarity or completeness.

Why It’s Effective

  • Improves Accuracy
  • Enhances Clarity
  • Reduces Bias and Harm
  • Encourages Deeper Reasoning

Self-Critique and Refinement Template

7. Prompt for Clarifying Questions

Before giving the full answer, instruct the model to ask you any missing or clarifying questions. For example:

“Before proceeding, list any questions you have about my request. Wait for my answers, then provide the final output.”

Clarifying Questions Template

8. Few-shot prompting

Few-shot prompting is a practice where you provide a language model with a small number of examples (or "shots") within the prompt to demonstrate the desired task, output style, or format. This technique leverages the model’s ability to learn and generalize from limited context, improving accuracy and consistency without requiring extensive fine-tuning or large datasets.

Key Aspects of Few-Shot Prompting

  • Examples as guidance: You include 2 to 5 diverse examples showing input-output pairs that illustrate how the model should respond.
  • Output structure and tone: Examples help the model understand the expected format, style, and level of detail.
  • In-context learning: The model uses these examples as a mini-training set to adapt its behavior on the fly.
  • Use cases: Specialized domains (legal, medical), strict output formatting, dynamic content creation, and personalized interactions benefit greatly from few-shot prompting.
  • Efficiency: It saves time and cost compared to full model fine-tuning while boosting performance on complex or niche tasks.

Few-Shot Prompting Template

9. Structured Output Specification

Unlike free-form text outputs, structured outputs enforce a schema or format, making responses predictable and reliable. Modern LLMs, especially with features like OpenAI’s Structured Outputs, can guarantee adherence to these schemas, significantly reducing errors and the need for complex post-processing. This technique is important, especially for cases where you want to feed the model's response to an API or other systems.

Structured Output Specification Prompt Template

All-in-One Template

FAQs

What are the most effective prompt engineering techniques for 2025?

TechniqueDescriptionPrimary Effect(s)
Zero-Shot PromptingInstructs the model to perform a task with clear instructions but no examples.Enables quick task execution without examples; good for straightforward tasks; relies on model knowledge.
Few-Shot PromptingProvides a few input-output examples within the prompt to demonstrate desired behavior.Improves accuracy and consistency on complex or nuanced tasks by guiding model behavior.
Chain-of-Thought (CoT)Encourages step-by-step reasoning by prompting the model to explain its thought process first.Enhances multi-step reasoning, reduces errors in logic and arithmetic tasks.
Meta PromptingUses AI to optimize or generate improved prompts dynamically.Automates prompt refinement, improving prompt effectiveness and reducing manual effort.
Self-Critique / ReflexionThe model reviews and iteratively improves its own responses.Boosts output quality, accuracy, and clarity through recursive self-improvement.
Role-Playing / PersonaAssigns the model a specific role or persona to influence tone and domain knowledge.Increases relevance, engagement, and domain-specific accuracy.
Context-Aware Decomposition (CAD)Breaks complex tasks into smaller, manageable steps while maintaining context.Improves handling of multi-part or complex queries by structuring reasoning.
Structured Output SpecificationRequests outputs in fixed formats like JSON, tables, or markdown.Ensures consistent, machine-readable, and easy-to-parse responses.
Ethical & Safety GuardrailsEmbeds instructions to avoid harmful, biased, or unsafe content and asks clarifying questions.Enhances responsible AI use by preventing harmful or biased outputs.
Directional Stimulus PromptingProvides guiding cues or hints to keep the model focused on the task.Increases relevance and alignment with user intent, especially in summarization.
Program-Aided Language Models (PAL)Integrates programming or computation within prompts for complex problem-solving.Enables handling of computation-intensive or simulation tasks with higher precision.
ReAct FrameworkCombines reasoning with task-specific actions (e.g., database queries) in prompts.Improves reliability and interactivity in tasks requiring reasoning plus external actions.

How can I write prompts that minimize hallucinations and improve factual accuracy?

Use the following techniques to reduce the hallucinations and improve the accuracy

  1. Few-Shot
  2. Role Playing
  3. Self-Critique and Refine
  4. Breaking down a complex prompt into smaller ones
  5. Request citations and Source Attribution
  6. Use According to.... in the prompt to instruct the model to do fact checking

What role does prompt clarity, specificity, and structure play in output quality?

  • Clarity: Clear prompts eliminate ambiguity, helping the AI grasp exactly what is being asked. This reduces misinterpretations and leads to more accurate, relevant, and meaningful responses.
  • Specificity: Providing detailed, specific instructions narrows the AI’s focus to the intended subject and desired outcome. Specific prompts minimize vague or broad queries that often yield generic or off-target answers. For example, asking for “a 200-word summary on the benefits of renewable energy, highlighting solar and wind power” is more effective than simply “Explain the topic.” Specificity drives precision and improves the efficiency of AI responses.
  • Structure: Using delimiters, bullet points, numbered lists, or clearly separated sections organizes complex instructions and multi-step tasks. Structured prompts help the AI parse and navigate the input more effectively, reducing errors and improving task comprehension.

What are the best practices for specifying output format and style to get consistent, machine-readable responses?

To get consistent, machine-readable responses, best practices for specifying output format and style include:

  • Be specific and descriptive about the desired output format (e.g., bullet points, JSON, CSV, markdown tables), style (formal, concise, friendly), length, and scope
  • Provide examples (few-shot prompting) demonstrating the exact input-output format you want, which helps the model learn the pattern and replicate it
  • Use structured output requests such as “Respond only in JSON format” or “Output this information as a CSV” to ensure machine-readability.
  • Include style instructions within the prompt to control tone and clarity, for example, “Write a clear and curt paragraph” or “Use formal language.”

What common pitfalls should I avoid in prompt engineering?

Some of the common mistakes in prompt engineering are:

  • Ignoring the Audience and Purpose
  • Over-Complicating or Overloading Prompts
  • Relying on Trial and Error (“Prompt and Pray”)
  • Ignoring Step-by-Step Instructions
  • Forgetting to Specify Output Format and Length
  • Neglecting Role Assignment and Persona Guidance
  • Overlooking Temperature and Creativity Settings

Conclusion

Mastering these advanced prompt-engineering tricks will help you—and your LLM—work smarter, not harder. By using politeness cues, personas, chain-of-thought, few-shot examples, structured formats, self-critique loops, prompt chaining, and clarification steps, you can dramatically boost accuracy, creativity, and consistency in every interaction.

Prompt Debugging

Even the best prompts can sometimes lead to unexpected or inaccurate responses. That’s why finding and fixing bugs in your prompts is a vital part of prompt engineering. Small changes in wording or structure can dramatically improve your AI’s accuracy and consistency.

Here’s why prompt debugging matters:

  • Boosts Accuracy: Tweaking prompts helps reduce hallucinations and errors.
  • Ensures Reliability: Debugged prompts perform consistently across varied inputs.
  • Saves Time & Money: Catching issues early prevents wasted API calls and manual corrections.
  • Builds Trust: Reliable outputs increase user confidence in your AI solutions.

Tailoring Prompt Engineering to Different AI Applications

Prompt engineering isn’t one-size-fits-all. Different AI applications demand distinct strategies to get the best results. Here’s how prompt engineering varies by use case, comparing AI Chatbots with Summarization tools:

Prompt engineering for chatbots focuses on clarifying user intents, managing dialogue flow, and maintaining context, while prompt engineering in summarization tools focuses on extracting key points and condensing information accurately.

Ready to dive deeper? Discover our Definitive Guide to LLM Prompt Engineering for hands-on examples

Happy prompting! 🚀

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