AI Content Hub

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

"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."

"Artificial intelligence is the science of making machines do things that would require intelligence if done by men."

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

"The measure of intelligence is the ability to change."

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.

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.

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.'

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.

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.

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.

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.

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.

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.

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).

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.

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.

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.

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.

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.

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

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.

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.