AI Content Hub

When making API calls to LLMs, implement exponential backoff for retries. This helps manage rate limits and temporary server issues gracefully without overwhelming the API.

If you plan to fine-tune an LLM, the quality of your training data is paramount. Even a small dataset of high-quality, relevant examples can be more effective than a large, noisy one.

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

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.

Training cutting-edge LLMs like GPT-4 can cost millions of dollars in computing resources, with some estimates placing it at $10-100 million for the largest models.

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.'

For the most cost-effective solution, GPT-3.5-Turbo often provides 80% of GPT-4's quality at 10% of the cost.

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.

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 identify potential risks in projects, business decisions, or strategies, and suggest mitigation approaches.

Content not available.

Challenge: Without using translation tools, craft a prompt that makes an LLM translate text between two uncommon language pairs (e.g., Finnish to Swahili). Compare the result with a professional translation service to assess accuracy.

AI is a tool. The choice about how it is used is ours.

Few-shot prompting improves LLM outputs by providing examples. Step 1: Define your task clearly. Step 2: Create 3-5 diverse, high-quality examples. Step 3: Format consistently (input→output). Step 4: Order examples from simple to complex. Step 5: Test and refine your examples based on model outputs.

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.

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

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.'

Challenge: Without using translation tools, craft a prompt that makes an LLM translate text between two uncommon language pairs (e.g., Finnish to Swahili). Compare the result with a professional translation service to assess accuracy.

The measure of intelligence is the ability to change.

If your application involves user-generated content that is then processed by an LLM, or if the LLM generates content for users, implement content filtering for harmful or inappropriate material.

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.

Few-shot prompting improves LLM outputs by providing examples. Step 1: Define your task clearly. Step 2: Create 3-5 diverse, high-quality examples. Step 3: Format consistently (input→output). Step 4: Order examples from simple to complex. Step 5: Test and refine your examples based on model outputs.

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

RAG combines pre-trained LLMs with external knowledge retrieval. The LLM's knowledge is augmented by fetching relevant information from a private or dynamic dataset before generating a response, reducing hallucinations and improving factual accuracy.

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

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

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.

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.

System prompts are often re-sent with every API call. Keep them concise to save tokens. Use abbreviations or keywords if the model is fine-tuned or understands them.

The real risk with AI isn't malice but competence. A super-intelligent AI will be extremely good at accomplishing its goals, and if those goals aren't aligned with ours, we have a problem.

Machine learning is the last invention that humanity will ever need to make.

An essential open-source library providing thousands of pretrained models for NLP, computer vision, and more. Great for both research and production.

A powerful framework for developing applications powered by language models, supporting document loading, prompt management, indexes for retrieval, chains, agents, and more.

Content not available.

When providing text for an LLM to process (e.g., summarize, translate), use clear delimiters like triple quotes ('''Text here''') or XML tags (<text>Text here</text>) to separate your instructions from the content.

Challenge: Prompt an LLM to explain a complex topic like quantum computing in simple terms a 5-year-old could understand. Evaluate its clarity and accuracy.

A powerful framework for developing applications powered by language models, supporting document loading, prompt management, indexes for retrieval, chains, agents, and more.

Different LLMs tokenize text differently. The same sentence can result in varying token counts across models, directly affecting API costs and context window usage.

Content not available.

When requesting translations, be precise about the target language and, if necessary, the dialect (e.g., 'Translate to French (Canadian)'). This ensures more accurate and culturally appropriate translations.

When prompting an LLM, be as clear and specific as possible. Define the desired format, length, tone, and role for the AI. For example, instead of 'Write about dogs,' try 'Write a 500-word blog post about the benefits of adopting a rescue dog, in a friendly and informative tone.'

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

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.'

Ask the LLM to write in the style of a specific author, genre, or era. For example, 'Write a poem about a cat in the style of Edgar Allan Poe.' This can lead to fun and surprising results.

For creative tasks, start with high temperature (0.8-1.0) to generate diverse ideas, then use lower temperature (0.2-0.4) to refine and polish the best concepts.

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.

LLM 'hallucinations' occur when models generate false or nonsensical information presented as factual. This happens because models predict plausible-sounding text rather than retrieving verified facts.

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

When you need structured responses, instruct the model to use XML tags. For example: 'Provide your answer in the following format: <summary>Brief overview</summary> <pros>List of advantages</pros> <cons>List of disadvantages</cons>'

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.

Use LLMs to analyze competitors by comparing features, pricing, marketing strategies, and market positioning based on publicly available information.

System prompts are often re-sent with every API call. Keep them concise to save tokens. Use abbreviations or keywords if the model is fine-tuned or understands them.

Most modern LLMs, including GPT and Gemini, are based on the Transformer architecture, introduced in the 2017 paper 'Attention Is All You Need.' Its key innovation is the attention mechanism.

Use LLMs to summarize academic papers, extract key findings, and identify research gaps or future directions in specific fields.

Generate sections of grant proposals including problem statements, methodology descriptions, and impact assessments for research or nonprofit projects.

Be mindful of how your AI application could be misused (dual-use problem). Design with safety and responsible AI principles in mind from the start.

Many LLM APIs allow you to specify which fields you want in the response. Only request the data you need (e.g., just the generated text, not full logprobs if unused) to reduce data transfer and processing.

Set up monitoring for your production LLM applications to track token usage patterns. This helps identify optimization opportunities and avoid unexpected costs when usage scales up.

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.

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

Ask the LLM to explain your problem back to you in different words. This often reveals assumptions or gaps in your problem description.

Do not send personally identifiable information (PII) or other sensitive data to public LLM APIs unless you understand their data usage policies and have appropriate safeguards.

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.

If LLM outputs are displayed to users or used in other systems, always validate and sanitize them to prevent injection attacks or the display of inappropriate content.

Generate realistic mock data for testing applications, including user profiles, transaction records, and sample content that matches your schema.

Monitor your LLM usage to optimize costs: 1) Implement logging for all API calls, 2) Track tokens per request and response, 3) Set up alerts for unusual usage patterns, 4) Create dashboards showing cost trends, 5) Analyze which prompts are most expensive and optimize them.

Generate press releases for company announcements, product launches, or events. Include all essential elements: headline, dateline, body, and boilerplate.

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.'

An open-source toolkit for optimizing prompts and evaluating LLM performance through A/B testing and analytics.

Ask LLMs to generate benchmarking code to measure the performance of different algorithms or implementations.

Content not available.

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

The term 'Generative AI' refers to artificial intelligence capable of generating novel content, such as text, images, audio, or video, rather than just analyzing or acting on existing data.

An open-source toolkit for optimizing prompts and evaluating LLM performance through A/B testing and analytics.

Don't expect the first output to be perfect. Use the LLM's initial response as a draft and provide feedback for refinement. Ask it to elaborate, shorten, rephrase, or change style.

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

Use delimiters like triple backticks (```), XML tags (<tag></tag>), or quotes ("") to clearly separate different parts of your prompt, such as instructions, context, examples, and input data.

When asking for code, provide surrounding code snippets or describe the existing architecture. This gives the LLM context, leading to more compatible and accurate code generation.

Challenge: Use an LLM to write a compelling short story (beginning, middle, end) using exactly 50 words. Experiment with different genres!

'AGI will be the most transformative technology humanity has ever created.' - Demis Hassabis (paraphrased).

The question of whether a computer can think is no more interesting than the question of whether a submarine can swim.

Content not available.

The measure of intelligence is the ability to change.

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

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

Improve complex reasoning by asking the model to 'think step by step' before giving its final answer. This simple instruction prompts LLMs to break down the problem-solving process, often resulting in more accurate answers.

When you need structured responses, instruct the model to use XML tags. For example: 'Provide your answer in the following format: <summary>Brief overview</summary> <pros>List of advantages</pros> <cons>List of disadvantages</cons>'

Challenge: Give an LLM a list of random ingredients you have on hand and ask it to generate a coherent and appealing recipe. Try to make it!

If you have many small, independent tasks for an LLM, batch them into a single API call if the model and API support it, rather than many separate calls. This can reduce overhead.

Use Socratic method prompts: 'Instead of giving me the answer, ask me questions that will help me discover the solution myself.'

MLOps platform for experiment tracking, model management, and collaboration in machine learning projects.

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

In conversational AI, the entire chat history is often sent with each new message, increasing token count. Summarize or truncate past history for long conversations to manage costs.

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

Stuck for ideas? LLMs are excellent brainstorming partners. Ask for blog post ideas, marketing slogans, plot twists, or research directions. Use their suggestions as a starting point.

Mistral's Mixtral-8x7B model offers an excellent balance of performance and cost for open models.

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.

Challenge: Write a compelling story using an LLM with these constraints: exactly 100 words, must include the words 'quantum', 'bicycle', and 'grandmother', and cannot use the letter 'e' in the last sentence.

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

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.

The pace of progress in artificial intelligence is incredibly fast. Unless you have direct exposure to groups like Deepmind, you have no idea how fast—it is growing at a pace close to exponential.

Before deploying an LLM application, define clear metrics to evaluate its performance (e.g., accuracy, relevance, helpfulness, F1 score for classification). This is crucial for ongoing improvement.

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.

A popular MLOps platform for tracking experiments, visualizing model performance, and managing machine learning workflows. Very useful for serious AI development and research.

Challenge: Create a system that can detect potential jailbreak attempts. First, research common jailbreak techniques (without implementing harmful ones). Then, design prompts that test the model's ability to recognize and respond appropriately to subtle manipulation attempts.

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.

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.

Set up monitoring for your production LLM applications to track token usage patterns. This helps identify optimization opportunities and avoid unexpected costs when usage scales up.

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.

Similar to text, image generation benefits from iteration. Start with a basic prompt, then refine it by adding details, styles (e.g., 'photorealistic', 'impressionist'), or camera angles.

GPT-4o excels at multi-modal tasks combining text, image, and code understanding.

Training a large language model can emit as much carbon as five cars over their lifetimes. However, inference (using a pre-trained model) is much more energy-efficient.

Modern AI systems can process multiple types of data simultaneously - text, images, audio, and video - enabling more sophisticated applications like visual question answering and audio-visual understanding.

When generating code with an LLM, ask it to explain the solution step-by-step before writing the code. This helps both your understanding and often leads to better quality code as the model thinks through the problem more methodically.

Use LLMs to design automated workflows for business processes, including decision trees, approval chains, and exception handling.

Experiment with different poetic forms and styles. Specify meter, rhyme scheme, theme, and mood for more targeted results.

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

Content not available.

For long texts, ask the LLM to summarize section by section, then summarize the summaries. This can be more effective than a single-shot summary of a very long document.

Large language models exhibit emergent abilities that weren't explicitly trained for, such as few-shot learning, chain-of-thought reasoning, and code generation, which appear at certain model scales.

LLMs are trained on vast datasets and can reflect societal biases. Critically evaluate outputs for fairness and consider techniques to mitigate bias in your applications.

Improve complex reasoning by asking the model to 'think step by step' before giving its final answer. This simple instruction prompts LLMs to break down the problem-solving process, often resulting in more accurate answers.

Don't always use the largest, most expensive model. Smaller, faster models can be sufficient and more cost-effective for simpler tasks. Evaluate tradeoffs between capability, speed, and cost.

When generating code with an LLM, ask it to explain the solution step-by-step before writing the code. This helps both your understanding and often leads to better quality code as the model thinks through the problem more methodically.

Ask LLMs to create concept maps or mind maps for complex topics, showing relationships between different ideas and concepts.

Content not available.

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

Ask the LLM to consider multiple perspectives: 'Analyze this issue from the viewpoint of [stakeholder A], [stakeholder B], and [stakeholder C].'

When using Claude, make use of its XML tags for better structure in your prompts.

Ask LLMs to review code for potential security vulnerabilities, but always follow up with human security experts for critical applications.

Sometimes, imposing unusual constraints can spark more creative LLM outputs. E.g., 'Write a story without using the letter E,' or 'Describe a color to someone who is blind.'

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.

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

The development of full artificial intelligence could spell the end of the human race.

AI will probably most likely lead to the end of the world, but in the meantime, there'll be great companies.

Content not available.

Content not available.

Generate database migration scripts or data transformation code when moving between different systems or updating schemas.

When prompting an LLM, be as clear and specific as possible. Define the desired format, length, tone, and role for the AI. For example, instead of 'Write about dogs,' try 'Write a 500-word blog post about the benefits of adopting a rescue dog, in a friendly and informative tone.'

Use a tokenizer tool (like the one on TokenCalculator.com!) to see how your text is split into tokens. This helps you understand why certain phrasing might consume more tokens and how to optimize it.

Challenge: Design a prompt that produces consistent, high-quality results across different LLM models (GPT, Claude, Gemini). Test and refine until you achieve similar outputs.

LLM 'hallucinations' occur when models generate false or nonsensical information presented as factual. This happens because models predict plausible-sounding text rather than retrieving verified facts.

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 question of whether a computer can think is no more interesting than the question of whether a submarine can swim.

Provide business or academic case studies to LLMs and ask for analysis, including problem identification, solution evaluation, and lessons learned.

Content not available.

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.

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

A free resource with machine learning papers, code, and evaluation tables. Great for staying up-to-date with the latest AI research.

For mathematical or logical reasoning problems, adding the phrase 'Let's think step by step' before the LLM generates its solution significantly improves accuracy.

Use LLMs to help design survey questions, ensuring they're unbiased, clear, and likely to generate useful data for your research goals.

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

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.

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

If you're not satisfied with an LLM's response, simply try regenerating it. Due to the probabilistic nature of LLMs, you might get a better answer on a subsequent attempt, especially with higher temperature settings.

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.

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.

A simple summarization prompt: 'Summarize the following text in three sentences: [Your text here]'. Experiment with sentence count and desired focus.

Use prompts like 'What would happen if...' or 'How would the outcome change if...' to explore alternative scenarios and their implications.

Modern LLMs are increasingly multimodal, meaning they can process and generate information across different types of data, such as text, images, audio, and even video.

LLMs can sometimes 'hallucinate,' meaning they generate plausible-sounding but incorrect or nonsensical information. Always verify critical information from LLM outputs.

When asking for a summary, specify the goal. Do you need key takeaways, an abstract, a summary for a specific audience, or a certain length? This helps the LLM produce a more useful summary.

Different LLMs use different tokenizers. The same piece of text can result in a varying number of tokens depending on the model, which impacts cost and context window usage. Tools like TokenCalculator help you see these differences.

An essential open-source library providing thousands of pretrained models for NLP, computer vision, and more. Great for both research and production.

A data framework for connecting custom data sources to LLMs, providing tools for ingestion, structuring, retrieval, and query interfaces.

Experiment with models that can process and generate not just text, but also images, audio, or video. This opens up many new application possibilities.

The Logic Theorist, created by Allen Newell, Herbert A. Simon, and Cliff Shaw in 1956, is often considered the first AI program. It was designed to mimic human problem-solving for proving mathematical theorems.

Training cutting-edge LLMs like GPT-4 can cost millions of dollars in computing resources, with some estimates placing it at $10-100 million for the largest models.

Content not available.

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

Define what you DON'T want as clearly as what you DO want. Example: 'Write a professional email. Do not use slang, emojis, or overly casual language.'

When asking an LLM to generate code, also ask it to include comments explaining the code. This improves readability and maintainability.

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

Create flashcards for studying by asking LLMs to generate question-answer pairs from your study materials or textbooks.

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

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

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

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

Training a large language model can emit as much carbon as five cars over their lifetimes. However, inference (using a pre-trained model) is much more energy-efficient.

LangChain is a framework for developing applications powered by language models. It provides modular components for managing prompts, chains, memory, and agents.

An LLM's context window is the amount of text (measured in tokens) it can consider at one time. Larger context windows allow for more complex tasks and longer conversations, but can also increase costs.

If you frequently use long model names in your prompts or code (e.g., 'gpt-3.5-turbo-instruct-0914'), consider using shorter aliases if your framework allows, or use variables to store them. This can slightly reduce token count in complex prompts.

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.'

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).

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.'

Use TokenCalculator.com to estimate costs before running expensive operations.

Generate compelling product descriptions that highlight features, benefits, and use cases. Specify target audience and desired tone.

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.

Content not available.

Ask LLMs to create analogies to help explain complex concepts by relating them to familiar, everyday experiences.

When trying to understand a topic, ask the LLM to explain it from different perspectives or viewpoints. This can deepen your comprehension.

The attention mechanism in transformers allows models to focus on relevant parts of the input when generating each token, enabling better understanding of long-range dependencies in text.

Challenge: Create a system that can detect potential jailbreak attempts. First, research common jailbreak techniques (without implementing harmful ones). Then, design prompts that test the model's ability to recognize and respond appropriately to subtle manipulation attempts.

When asking an LLM to analyze or extract data, clearly specify the desired output format (e.g., JSON, CSV, markdown table). This makes the output easier to parse and use programmatically.

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.

Never blindly trust code generated by an LLM. Always thoroughly test it for correctness, security vulnerabilities, and performance issues before using it in production.

A data framework for connecting custom data sources to LLMs, providing tools for ingestion, structuring, retrieval, and query interfaces.

Generate scripts for educational videos, tutorials, or presentations. Include timing cues, visual descriptions, and clear narrative structure.

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.

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

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

Provide meeting transcripts to LLMs and ask them to generate structured meeting minutes with action items, decisions, and key discussion points.

Implement caching strategies for repeated queries to reduce API calls and costs.

By far, the greatest danger of Artificial Intelligence is that people conclude too early that they understand it.

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.

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?').

When using LLM-generated content publicly, consider attributing it as AI-assisted or AI-generated, especially in contexts where transparency is important (e.g., news, academic writing).

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

Control LLM output creativity using 'temperature' (randomness) and 'top_p' (nucleus sampling). Lower temperature means more deterministic, focused output. Higher values increase creativity/randomness.

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.'

Don't expect the first output to be perfect. Use the LLM's initial response as a draft and provide feedback for refinement. Ask it to elaborate, shorten, rephrase, or change style.

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'.

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'.'

LLM stands for Large Language Model. 'Large' refers to the vast number of parameters the model has and the extensive data it was trained on.

If there are several ways to implement a feature, ask the LLM to provide a few different code solutions along with their pros and cons. This can help you choose the best approach.

AI is a tool. The choice about how it is used is ours.

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.

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.

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.

Provide your story setup and ask the LLM to suggest unexpected but logical plot twists that could enhance your narrative.

The real risk with AI isn't malice but competence. A super-intelligent AI will be extremely good at accomplishing its goals, and if those goals aren't aligned with ours, we have a problem.

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

If an LLM struggles with a complex task, break it into smaller, simpler sub-tasks. Solve each part sequentially, and then combine the results. This is often more effective than one large prompt.

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

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.

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

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

Be aware of API rate limits (requests per minute/day). Design your application to handle these gracefully, perhaps with retries and exponential backoff.

For complex reasoning, you can often trigger CoT by simply appending 'Let's think step by step.' to your prompt. The model will then show its reasoning process before the final answer.