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Treat your prompts as code. Use version control (like Git) to track changes, iterate, and collaborate on prompt engineering.

When asking an LLM to write code, specifically request that it include error handling (e.g., try-catch blocks, input validation).

Incorporate keywords that suggest the desired style or tone, e.g., 'formal', 'casual', 'humorous', 'academic', 'empathetic'.

If you perform a specific task repeatedly with long prompts, consider fine-tuning a smaller model. This can significantly reduce token count and improve performance for that task.

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.

If a complex prompt isn't working, simplify it. Remove parts one by one to identify which section is causing the issue.

To generate more detailed and nuanced content, first ask for a brief outline, then ask the LLM to expand on each point, and then ask it to add specific examples or data. This is a form of 'Chain of Density'.

Review your prompts for ambiguous words or phrases that could be interpreted in multiple ways. Strive for explicitness.

When an LLM generates code, ask it to also generate unit tests for that code. This helps ensure correctness and makes future refactoring safer.

Use an LLM to simulate dialogues for practicing conversations, interviews, or customer service interactions. Define the roles and scenario.

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.

Ask the LLM to generate several different versions or drafts of a creative piece. You can then pick the best one or combine elements from different drafts.

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.

If LLM-generated code has errors, provide the error message back to the LLM and ask it to debug and fix the code. Iterate until it works.

In chat applications, analyze how much of the conversation history is repeated tokens. Strategies like summarization can reduce this significantly.

LLMs can be surprisingly good at translating code snippets from one programming language to another. Useful for learning new languages or migrating projects.

In a production environment, A/B test different prompt variations to empirically determine which ones yield the best results for your key metrics.

If incorporating user input into prompts, be aware of prompt injection vulnerabilities. Sanitize user inputs or use techniques to separate instructions from user data.

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?'

Use LLMs to help build fictional worlds by generating names, cultures, histories, or even maps based on your descriptions and requirements.

Provide a working piece of code to an LLM and ask for suggestions on how to optimize it for performance or readability.

If your prompt uses domain-specific jargon or acronyms the LLM might not know, provide a small glossary or definitions within the prompt.

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.

LLMs are excellent at generating boilerplate code for common patterns (e.g., setting up a new class, a basic API endpoint, HTML structure).

To explore different sides of an argument, ask an LLM to take a specific stance on a topic and then debate it. Instruct it to provide evidence or reasoning for its points.

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.

Use LLMs to generate personalized content for users, such as tailored recommendations, custom learning plans, or individualized messages.

Beyond exact-match caching, consider semantic caching. If a new prompt is semantically similar to a previously cached one, you might be able to reuse the old response, potentially saving an API call.

If you have writer's block, describe your general idea or last sentence to an LLM and ask for suggestions on what could happen next or different ways to phrase something.

For recurring tasks, create prompt templates with placeholders for variable inputs. This ensures consistency and makes it easier to automate prompt generation.

LLMs can assist in refactoring legacy code by suggesting modernizations, improving readability, or even translating to a new language (with careful review).

Start your prompt with 'Assume the role of a [expert/character].' This helps the LLM adopt the desired persona and knowledge base more effectively.

Ask an LLM to generate practice problems or quiz questions for a topic you're learning, along with answers and explanations.

Implement fallback strategies in your application for when the LLM fails, returns an error, or provides an unsatisfactory response. Don't let it be a single point of failure.

When performing sentiment analysis, ask the LLM to not just classify as positive/negative/neutral, but also to identify specific emotions or nuances in the text.

Use LLMs to paraphrase text to avoid plagiarism, simplify complex language, or adapt content for different audiences. Always review for accuracy.

For applications like chatbots, use streaming API responses if available. This allows text to be displayed token by token as it's generated, improving perceived responsiveness.

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.'

Provide code to an LLM and ask it to generate documentation (e.g., docstrings, comments, or a README section) for that code.

A classic prompt technique: ask the LLM to 'Explain [complex topic] like I'm 5 years old.' This forces very simple, clear explanations.

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.

Use LLMs to generate synthetic data for training other machine learning models, especially when real-world data is scarce or sensitive. Validate quality carefully.

Provide context about characters and a situation, and ask the LLM to write dialogue between them. Specify tone and subtext if needed.

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).

Describe the pattern you want to match in natural language, and ask an LLM to generate the corresponding regular expression. Test it thoroughly.

Example: 'I am a software developer. Pretend to be a senior architect and review my proposed design [design details].' Helps frame interaction.

Use LLMs to help moderate user-generated content by flagging potentially harmful, inappropriate, or off-topic posts for human review.

For complex tasks, reveal information progressively. Start with basic requirements, get initial output, then add more specific constraints or details.