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Reflection or Self-Critique Prompts
Ask the LLM to critique its own previous answer and then improve it. For example: 'Here is your previous response: [response]. Please identify any flaws and provide an improved version.'
Grounding with Facts
If accuracy on specific facts is crucial, provide those facts within the prompt. This helps to ground the LLM and reduce the chance of hallucinations on those specific points.
Version Control Prompts
Treat your prompts as code. Use version control (like Git) to track changes, iterate, and collaborate on prompt engineering.
Use Analogies for Complex Explanations
If you need an LLM to explain a complex topic simply, ask it to use an analogy. For example, 'Explain quantum entanglement using an analogy involving a pair of gloves.'
Emotional Intelligence Prompting
When dealing with sensitive topics, instruct the LLM to consider emotional context: 'Respond with empathy and understanding, considering the emotional state of someone facing this situation.'
The 'Assume Role' Technique
Start your prompt with 'Assume the role of a [expert/character].' This helps the LLM adopt the desired persona and knowledge base more effectively.
Self-Correction Prompts
Ask the LLM to review its own previous output for errors or areas of improvement. 'Review your previous response. Are there any inaccuracies or ways to make it clearer?'
Constraint Relaxation
If initial constraints are too restrictive and yield poor results, gradually relax them: 'If the previous constraints are too limiting, suggest the closest possible alternative.'
Use Keywords for Style/Tone
Incorporate keywords that suggest the desired style or tone, e.g., 'formal', 'casual', 'humorous', 'academic', 'empathetic'.
ReAct Prompting (Reason + Act)
A more advanced technique where the LLM is prompted to generate both reasoning traces and actions to take to solve a problem, often interacting with external tools.
Anthropic Claude Documentation
Comprehensive documentation for Claude AI, including prompt engineering tips, safety guidelines, and API usage examples.
Prompt Engineering Guide
A comprehensive resource for learning prompt engineering techniques, best practices, and common patterns for getting the most out of LLMs.
Request Multiple Options
If you want diverse ideas or solutions, ask the LLM to generate several options (e.g., 'Provide 3 different headlines for this article.'). This gives you more to choose from and refine.
Use 'Stop Sequences'
Use stop sequences to tell the model when to stop generating text. This is useful for preventing run-on sentences or irrelevant content after the desired output.
Minimal Token Maximum Impact
Challenge: Create the most effective prompt for a complex task using the fewest possible tokens. Test different approaches and measure both output quality and token efficiency.
Break Down Complex Tasks
For complex problems, break them into smaller, manageable steps. Use an LLM for each step, then combine the results. This is often more effective than one large, complicated prompt.
Be Specific About Length and Detail
If you need a concise summary or a detailed explanation, specify the desired length (e.g., 'in one paragraph,' 'in 100 words,' 'provide a comprehensive overview'). This guides the LLM's output.
Few-Shot Prompting
Provide a few examples (input/output pairs) in your prompt to guide the LLM's response. This is called few-shot prompting and can significantly improve performance on specific tasks.
Provide a 'Glossary' for Specific Terms
If your prompt uses domain-specific jargon or acronyms the LLM might not know, provide a small glossary or definitions within the prompt.
Avoid Ambiguity
Review your prompts for ambiguous words or phrases that could be interpreted in multiple ways. Strive for explicitness.
Progressive Disclosure
For complex tasks, reveal information progressively. Start with basic requirements, get initial output, then add more specific constraints or details.
Role-Playing Prompts
Instruct the LLM to adopt a specific persona or role (e.g., 'You are a helpful assistant specializing in physics.'). This can significantly shape the tone, style, and content of its responses.
Embrace Constraints
Adding constraints to your prompt (e.g., word count, specific keywords to include/exclude, format requirements) can often lead to more focused and useful outputs from the LLM.
Prompt Engineering Guide
A comprehensive resource for learning prompt engineering techniques, best practices, and common patterns for getting the most out of LLMs.
Constraint Satisfaction Prompts
Clearly list all constraints the output must satisfy. For example, 'Write a poem about a cat that is exactly 10 lines long and mentions the moon.'
Context Window Awareness
Understand the context window limit of the model you are using. Information outside this window will be ignored. For long interactions, summarization or RAG is key.
Template Prompts
For recurring tasks, create prompt templates with placeholders for variable inputs. This ensures consistency and makes it easier to automate prompt generation.
Chain Prompts for Multi-Step Tasks
For complex tasks, chain multiple prompts together. The output of one LLM call becomes the input (or part of the input) for the next. This allows for sophisticated workflows.
PromptTools
An open-source toolkit for optimizing prompts and evaluating LLM performance through A/B testing and analytics.
Use Output Priming
Start the desired output for the LLM. For instance, if you want a list, start your prompt with the beginning of the list: 'Here are the steps:
1.' This can guide the model effectively.
Zero-Shot vs. Few-Shot Prompting
Zero-shot prompting asks an LLM to perform a task without prior examples. Few-shot prompting provides 1-5 examples within the prompt, often significantly improving performance on novel tasks.
Iterate on Your Prompts
Don't expect the perfect response on your first try. Prompt engineering is an iterative process. Refine your prompts based on the LLM's output to improve results. Small changes can make a big difference.
Specify the Target Audience
When generating content, tell the LLM who the target audience is (e.g., 'Explain this to a 5-year-old,' or 'Write this for an expert audience.'). This helps tailor the complexity and tone.
Instruction Priming
Start your prompt with a clear instruction like 'Translate the following text to French:' before providing the text itself. This primes the model for the task.
Counterfactual Reasoning
Use prompts like 'What would happen if...' or 'How would the outcome change if...' to explore alternative scenarios and their implications.
Basic Prompt for Summarization
A simple summarization prompt: 'Summarize the following text in three sentences: [Your text here]'. Experiment with sentence count and desired focus.
Specify 'Don't Know' Option
To reduce hallucinations, explicitly instruct the LLM to say 'I don't know' or a similar phrase if it cannot answer a question confidently or accurately based on the provided context.
The 'Pretend To Be' Prompt
Example: 'I am a software developer. Pretend to be a senior architect and review my proposed design [design details].' Helps frame interaction.
Key Info Placement
When processing long documents, try to place key instructions or questions at the beginning or end of the input text. Models sometimes pay more attention to the start/end of context.
Experiment with Temperature Settings
The 'temperature' parameter controls randomness. Lower values (e.g., 0.2) make output more deterministic and focused. Higher values (e.g., 0.8) increase creativity and diversity. Adjust it based on your task.
Structured Input for Complex Tasks
For complex tasks involving multiple pieces of information, provide the input in a structured format (e.g., using JSON within the prompt, or clear headings) to help the LLM parse it correctly.
Using System Prompts
Many LLM APIs allow for a 'system prompt' or 'system message' which sets the overall behavior, persona, or instructions for the LLM throughout a conversation, separate from user prompts.
Structured Prompts for Complex Tasks
For intricate tasks, consider a structured prompt with sections like: ROLE, CONTEXT, TASK, OUTPUT_FORMAT, EXAMPLES. This organization helps the LLM understand requirements better.
Socratic Questioning
Use Socratic method prompts: 'Instead of giving me the answer, ask me questions that will help me discover the solution myself.'
Negative Prompts: Specify What NOT To Do
Sometimes it's effective to tell the LLM what to avoid. For example, 'Write a product description. Do not use clichés or overly technical jargon.'
PromptTools
An open-source toolkit for optimizing prompts and evaluating LLM performance through A/B testing and analytics.
Golden Rule: Garbage In, Garbage Out
The quality of your LLM's output is highly dependent on the quality of your input prompt. Clear, well-structured, and relevant prompts lead to better results.
Specify Output Format
If you need structured output (e.g., JSON, XML, a list), explicitly ask the LLM to provide it in that format. This often yields better and more parsable results. Example: 'Provide your answer as a JSON object with keys 'name' and 'summary'.'
Iterative Refinement Protocol
Establish a protocol: 'After each response, I'll provide feedback. Use this feedback to improve your next response while maintaining the core requirements.'
The 'What, Why, How' Framework
Structure prompts by defining: WHAT you want the LLM to do, WHY it's important (context), and HOW it should do it (format, style, constraints).
Use 'Let's think step by step' for Math Problems
For mathematical or logical reasoning problems, adding the phrase 'Let's think step by step' before the LLM generates its solution significantly improves accuracy.
Perspective Taking
Ask the LLM to consider multiple perspectives: 'Analyze this issue from the viewpoint of [stakeholder A], [stakeholder B], and [stakeholder C].'
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