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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.

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

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

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'.'

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.'

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.'

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.'

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.

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.

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.

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).

LLMs can be great for learning. Ask them to explain complex concepts in simple terms, quiz you on topics, or provide examples. Specify your current understanding level for tailored explanations.

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.

Use LLMs to generate first drafts of emails, reports, or other documents. Provide key points and desired tone, then refine the output. This can save significant time.

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

For tasks requiring up-to-date or domain-specific knowledge, use RAG. Retrieve relevant information from a knowledge base first, then feed it to the LLM as context to generate the answer.

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.

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.