Ultimate Top100 tips and tricks for Prompts Engineers

Prompt Engineering: Top100 tips and tricks


Welcome to the world of prompt engineering, where you’ll discover the secrets to crafting incredible AI conversations. In this informative guide, we’ll walk you through the top 100 tips and tricks to help you become a prompt engineering pro, even if you’re a newbie.

Are you ready to dive into the fascinating world of AI-powered assistants like ChatGpt, and Bard? Great! We’ll show you how to make the most out of your interactions and have some fun along the way. So, let’s get started on this exciting journey of prompt engineering together!

Remember, prompt engineering is all about finding the perfect words to get the best responses from AI. It’s like being a magician who knows just what to say to make the magic happen! But don’t worry, you don’t need a wand for this. All you need is your curiosity and eagerness to learn.

Throughout this guide, we’ll cover a wide range of techniques that will help you become a master at creating conversations with AI. From using the right words to getting creative with your prompts, we’ve got you covered. So, whether you’re a student, a budding developer, or just someone who loves exploring AI, this guide is for you!

Get ready to unlock the full potential of prompt engineering and take your AI conversations to a whole new level. Exciting, right? Let’s jump right in and start our adventure together!

Top 100 Tips and tricks for writing advance level prompts

Certainly! Here are 100 tips and tricks for aspiring prompt engineers:

  1. Understand the Basics:
    Familiarize yourself with the fundamental concepts of prompt engineering, including input formatting, context utilization, and model fine-tuning.
  2. Context is Key:
    Pay attention to context in your prompts; it helps the model generate more coherent and relevant responses.
  3. Experiment with Length:
    Vary the length of your prompts to see how it impacts the model’s output. Sometimes concise prompts yield better results.
  4. Fine-Tune Your Model:
    If possible, explore fine-tuning your language model on specific domains or topics for more tailored responses.
  5. Balance Specificity and Generality:
    Find the right balance between specific prompts and more general ones to achieve the desired level of detail in responses.
  6. Explore Temperature Settings:
    Adjust the temperature parameter to control the randomness of the generated outputs. Lower values make responses more focused; higher values introduce more randomness.
  7. Use Top-p and Top-k Sampling:
    Experiment with top-p (nucleus) and top-k sampling techniques to control the diversity of generated responses.
  8. Craft Open-Ended Prompts:
    Encourage creativity by crafting open-ended prompts that allow the model to explore various possibilities.
  9. Validate Outputs:
    Always validate the generated outputs to ensure they align with your intent and are appropriate for the context.
  10. Practice Ethical AI:
    Be mindful of potential biases in language models and take steps to mitigate them in your prompts.
  11. Incorporate User Instructions:
    Clearly specify instructions within your prompts to guide the model towards desired behaviors.
  12. Explore Preprocessing Techniques:
    Experiment with text preprocessing techniques to enhance the model’s understanding of input data.
  13. Utilize Special Tokens:
    Leverage special tokens like <|endoftext|> to indicate the end of a prompt or context.
  14. Iterate and Refine:
    Prompt engineering is an iterative process. Continuously refine your prompts based on model feedback.
  15. Incorporate Historical Context:
    Introduce historical context in prompts to prompt the model to consider past information.
  16. Learn from Model Responses:
    Analyze model responses to understand their strengths and limitations, helping you improve your prompts.
  17. Explore Multi-Turn Conversations:
    Engage in multi-turn conversations with the model to observe how it maintains context over several interactions.
  18. Combine Prompts:
    Combine multiple prompts to create complex queries or scenarios, encouraging richer responses.
  19. Consider Negative Examples:
    Include negative examples in your prompts to guide the model away from undesired behaviors.
  20. Stay Informed:
    Keep up with the latest advancements in prompt engineering and AI research to refine your strategies.
  21. Think Like a User:
    Put yourself in the user’s shoes when crafting prompts to ensure the model generates user-friendly responses.
  22. Explore Different Models:
    Experiment with different language models to find the one that best fits your specific use case.
  23. Understand Prompt Impact:
    Recognize the impact of prompts on model behavior, realizing that small changes can yield significant variations in responses.
  24. Validate Against Guidelines:
    Ensure that your prompts align with ethical guidelines and community standards.
  25. Consider Domain Specificity:
    Tailor your prompts to specific domains to enhance the model’s expertise in those areas.
  26. Use Neutral Language:
    Craft prompts in neutral language to minimize biases and ensure fair responses.
  27. Experiment with GPT Variants:
    Explore different variants of GPT models to understand their unique capabilities and limitations.
  28. Use Custom Tokens:
    Introduce custom tokens to mark specific sections or cues within your prompts.
  29. Include Common Scenarios:
    Incorporate prompts that involve common scenarios to improve the model’s practical utility.
  30. Test Robustness:
    Test the robustness of your prompts by introducing variations and assessing how the model adapts.
  31. Balance Flexibility and Control:
    Strike a balance between allowing the model flexibility and maintaining control over the generated content.
  32. Evaluate Trade-Offs:
    Understand the trade-offs between model complexity, response quality, and inference speed.
  33. Be Patient:
    Generating optimal prompts takes time. Be patient and persistent in refining your approach.
  34. Diversify Training Data:
    Utilize diverse training data to expose the model to a wide range of topics and writing styles.
  35. Avoid Ambiguity:
    Craft prompts that minimize ambiguity to obtain clearer and more accurate responses.
  36. Leverage Conditional Prompts:
    Use conditional prompts to guide the model’s response based on specific conditions or scenarios.
  37. Experiment with Prompt Rewriting:
    Rewrite prompts in different ways to explore how variations influence the model’s output.
  38. Incorporate Feedback Loops:
    Establish feedback loops with users to continuously improve prompt quality based on real-world interactions.
  39. Collaborate with Experts:
    Collaborate with domain experts to refine prompts related to specific industries or fields.
  40. Understand Model Biases:
    Be aware of potential biases in the language model and take proactive steps to address them in prompts.
  41. Explore Transfer Learning:
    Investigate transfer learning techniques to adapt pre-trained models to your specific needs.
  42. Integrate User Preferences:
    Consider user preferences when designing prompts to enhance the personalized nature of responses.
  43. Prioritize Key Information:
    Structure prompts to prioritize key information, helping the model focus on crucial details.
  44. Validate Cross-Model Compatibility:
    If using multiple models, ensure that prompts are compatible and effective across different architectures.
  45. Consider Audience Knowledge:
    Tailor prompts to the level of knowledge expected from the audience for more relevant responses.
  46. Experiment with Prompt Variability:
    Introduce variability in your prompts to gauge how the model adapts to different input styles.
  47. Use External Knowledge Sources:
    Integrate prompts with external knowledge sources to enhance the model’s information base.
  48. Avoid Overfitting:
    Guard against overfitting by crafting prompts that encourage generalized rather than overly specific responses.
  49. Explore Zero-Shot Learning:
    Experiment with zero-shot learning approaches to train the model on tasks without explicit examples.
  50. Facilitate User Guidance:
    Provide guidance to users on how to frame effective prompts for desired outcomes.
  51. Incorporate Temporal Elements:
    Introduce temporal elements in prompts to prompt the model to consider time-sensitive information.
  52. Validate Response Coherence:
    Ensure that responses are coherent and contextually appropriate, avoiding disjointed or nonsensical replies.
  53. Explore Query Reformulation:
    Experiment with reformulating queries to assess how different formulations impact model responses.
  54. Fine-Tune for Specific Vernacular:
    If applicable, fine-tune models to recognize and respond in specific vernaculars or regional language variations.
  55. Use Reinforcement Learning:
    Explore reinforcement learning techniques to fine-tune models based on real-world feedback.
  56. Balance Complexity and Simplicity:
    Balance the complexity of prompts with the need for clear and easily interpretable responses.
  57. Verify Information Accuracy:
    Cross-verify information generated by the model against reliable sources to ensure accuracy.
  58. Facilitate Incremental Learning:
    Design prompts that facilitate incremental learning, allowing the model to build on previous knowledge.
  59. Introduce Conceptual Prompts:
    Incorporate conceptual prompts to prompt the model to generate explanations and insights.
  60. Optimize for Specific Outputs:
    Fine-tune prompts to optimize for specific types of outputs, such as summaries, answers, or creative content.
  61. Experiment with Multimodal Inputs:
    Explore prompts that incorporate both text and other modalities, like images or audio, for a richer understanding.
  62. Consider Inference Costs:
    Be mindful of computational costs and choose prompts that balance model accuracy with inference speed.
  63. Evaluate Prompt Impact on Bias:
    Assess how different prompts influence the model’s potential bias and adjust accordingly.
  64. Experiment with Unstructured Prompts:
    Test the model’s ability to handle unstructured prompts by introducing varied sentence structures and formats.
  65. Leverage Model Prompts as Seeds:
    Use model-generated prompts as seeds for subsequent queries, allowing for a more dynamic and evolving conversation.
  66. Optimize for Task-Specific Metrics:
    Fine-tune prompts based on task-specific metrics, whether it’s accuracy, relevance, or other performance indicators.
  67. Introduce Challenges in Prompts:
    Challenge the model with complex or ambiguous scenarios to test its problem-solving capabilities.
  68. Encourage User Feedback:
    Actively seek user feedback on generated content to identify areas for prompt improvement.
  69. Facilitate Transferable Skills:
    Design prompts that encourage the model to transfer skills learned in one context to another.
  70. Consider Conversational Dynamics:
    Craft prompts that simulate conversational dynamics, including interruptions and topic shifts.
  71. Promote Positive Language:
    Use prompts that encourage the model to generate positive and constructive language.
  72. Fine-Tune for Domain Specificity:
    If working within a specific domain, fine-tune models to excel in the nuances of that field.
  73. Verify Legal and Ethical Compliance:
    Ensure that prompts align with legal and ethical standards, especially in sensitive domains.
  74. Explore Dynamic Context Update:
    Experiment with prompts that dynamically update context to see how the model adapts to changing information.
  75. Optimize for Real-Time Applications:
    Fine-tune prompts to meet the demands of real-time applications, considering both speed and accuracy.
  76. Facilitate Incremental Training:
    Design prompts that allow for incremental training, enabling the model to adapt to evolving requirements.
  77. Understand Latency Tolerance:
    Gauge the tolerance for latency in your applications and design prompts accordingly.
  78. Explore Transferable Knowledge:
    Experiment with prompts that encourage the model to transfer knowledge across different domains.
  79. Fine-Tune for Multilingual Support:
    If applicable, fine-tune models to support multilingual prompts for a global audience.
  80. Evaluate Memory Capacity:
    Understand the model’s memory limitations and design prompts that maximize information retention.
  81. Facilitate Incremental Task Complexity:
    Gradually increase task complexity in prompts to assess the model’s adaptability to challenging scenarios.
  82. Consider User Intent Recognition:
    Design prompts that prompt the model to recognize and respond to user intents effectively.
  83. Validate External Entity Recognition:
    If relevant, assess the model’s ability to recognize and respond to external entities mentioned in prompts.
  84. Fine-Tune for Specific Genres:
    Customize prompts to suit specific genres or writing styles for more contextually appropriate responses.
  85. Encourage Emotion Recognition:
    Experiment with prompts that prompt the model to recognize and respond to emotional cues.
  86. Optimize for Visual Descriptions:
    If working with visual prompts, fine-tune models to generate detailed and accurate visual descriptions.
  87. Understand Trade-Offs in Complexity:
    Recognize the trade-offs between prompt complexity and the potential for ambiguous or unexpected outputs.
  88. Explore Hyperparameter Tuning:
    Experiment with hyperparameter tuning to optimize the model’s behavior for specific tasks.
  89. Verify Responsiveness:
    Assess the model’s responsiveness to prompts in real-time scenarios, ensuring timely and relevant outputs.
  90. Fine-Tune for Query Refinement:
    If using iterative queries, fine-tune the model to understand and respond to refined follow-up questions.
  91. Evaluate Model Robustness:
    Test the model’s robustness by introducing noise or irrelevant information in prompts.
  92. Facilitate Natural Language Understanding:
    Design prompts that prompt the model to showcase improved natural language understanding.
  93. Optimize for Educational Content:
    Fine-tune prompts for generating educational content, emphasizing clarity and coherence.
  94. Explore Code Generation:
    Experiment with prompts that encourage the model to generate code snippets or programming-related content.
  95. Facilitate Controlled Creativity:
    Craft prompts that strike a balance between creativity and adherence to specified guidelines.
  96. Optimize for Dialogue Flow:
    Fine-tune prompts to enhance the model’s ability to maintain coherent and contextually relevant dialogue.
  97. Explore Proactive Suggestions:
    Design prompts that prompt the model to offer proactive suggestions or recommendations.
  98. Facilitate Domain Adaptation:
    Experiment with prompts that facilitate the model’s adaptation to different domains or industries.
  99. Optimize for Ambiguity Resolution:
    Fine-tune prompts to prompt the model to effectively resolve ambiguity in queries or scenarios.
  100. Stay Curious and Experiment:
    Prompt engineering is an evolving field. Stay curious, experiment with new ideas, and be open to continuous learning to refine your prompt engineering skills.


In conclusion, being a prompt engineer is like being a wizard with words and questions. We’ve explored a bunch of cool tricks and tips that can make our talking computer friend, the language model, understand and answer our questions better. It’s like teaching a really smart robot to chat with us! Remember, it’s important to be patient and try different ways of asking questions to get the best answers. Keep learning, stay curious, and have fun talking to the computer magic!

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