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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.

After an LLM analyzes data, ask it to suggest appropriate ways to visualize that data (e.g., 'What type of chart would best represent these trends?').

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.

Provide a piece of content (like a blog post or documentation) to an LLM and ask it to generate a list of Frequently Asked Questions (FAQs) based on it.

Never embed API keys directly in client-side code or public repositories. Use environment variables or secure secret management services.

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).

Provide two or more texts to an LLM and ask it to generate a summary that highlights the key differences and similarities between them.

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.

Understand the difference. Extractive summaries use exact sentences from the source. Abstractive summaries generate new sentences. Specify which you prefer, or let the LLM decide if it's good at both.

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.

Ask an LLM to create comprehensive study guides from textbooks, lecture notes, or research papers. Include key concepts, definitions, and practice questions.

When working with APIs, ask LLMs to generate example code for common integration patterns, including authentication, error handling, and data parsing.

Use LLMs to generate social media posts, hashtags, and captions tailored to different platforms and audiences. Specify tone, length, and platform requirements.

Provide statistical results to an LLM and ask it to explain the findings in plain language, including practical implications and limitations.

Ask the LLM to think about its thinking process: 'Before answering, consider what approach would be most effective for this problem and explain your reasoning strategy.'

Use LLMs to draft customer support responses, categorize tickets, and suggest solutions based on common issues and knowledge base articles.

Ask LLMs to help develop fictional characters by generating backstories, personality traits, motivations, and character arcs based on your initial concepts.

Use LLMs as conversation partners for language learning. Practice dialogues, get grammar corrections, and learn cultural context for different languages.

Provide slow database queries to an LLM and ask for optimization suggestions, including index recommendations and query restructuring.

Generate email marketing templates for different purposes: welcome emails, newsletters, promotional campaigns, and follow-up sequences.

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.'