AI Content Hub

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.

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.

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.

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.

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

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 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.'

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.

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

Generate user manuals, API documentation, and technical guides. Ensure clarity for your target audience's technical level.

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.

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

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.

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

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.

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.

Create reusable prompt templates: 1) Identify common prompt patterns in your application, 2) Extract variable parts into placeholders, 3) Create template functions with parameter validation, 4) Build a library of tested templates, 5) Implement version control for template changes.

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.

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.

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.

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

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

Training state-of-the-art large language models requires massive datasets, significant computational power (often thousands of GPUs), and can cost millions of dollars.

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.

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.

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

Content not available.

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.

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.

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

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.

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.

When asking for code, specify the programming language and, if relevant, the version (e.g., 'Python 3.9', 'JavaScript ES6'). This helps avoid ambiguity and deprecated features.

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

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

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>'

Content not available.

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

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

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

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.

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

For intricate tasks, consider a structured prompt with sections like: ROLE, CONTEXT, TASK, OUTPUT_FORMAT, EXAMPLES. This organization helps the LLM understand requirements better.

If you need a concise summary or a detailed explanation, specify the desired length (e.g., 'in one paragraph,' 'in 100 words,' 'provide a comprehensive overview'). This guides the LLM's output.

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.

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

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

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.

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

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

An open-source embedding database that makes it easy to build LLM applications with semantic search capabilities.

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.

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.'

Content not available.

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.

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

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.

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

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.

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

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

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.

Ask LLMs to generate configuration files for various tools and frameworks based on your requirements and best practices.

Content not available.

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.

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.

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.

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

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.

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

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).

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.

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.

Consider the latency requirements of your application. Larger models are often slower. Choose a model that balances performance with speed.

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.

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

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.

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

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

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

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.

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.

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 initial constraints are too restrictive and yield poor results, gradually relax them: 'If the previous constraints are too limiting, suggest the closest possible alternative.'

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.'

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.

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.

Adding constraints to your prompt (e.g., word count, specific keywords to include/exclude, format requirements) can often lead to more focused and useful outputs from the LLM.

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

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

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.

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

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

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

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.

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

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

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.

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

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

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

Content not available.

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.'

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.

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?'

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

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.

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

Content not available.

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

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.

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

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.

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

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.

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

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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

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

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.

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.

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

Content not available.

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.

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

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

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.

Retrieval Augmented Generation (RAG) combines the knowledge of LLMs with specific document retrieval. To build a basic RAG system: 1) Process your documents into embeddings, 2) Store them in a vector database, 3) When queried, retrieve relevant content based on similarity, 4) Augment your prompt with this content, and 5) Generate contextually informed responses.

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?').

The measure of intelligence is the ability to change.

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).

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.

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.

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

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.

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

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.

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.'

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.'

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

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

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.

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.

The measure of intelligence is the ability to change.

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.

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

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

When building multilingual AI applications, test responses not just for linguistic accuracy but also for cultural appropriateness and nuances in each target language and region.

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

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

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

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.

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.

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

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.

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.

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.

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'.'

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!

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

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

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.

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

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

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.

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.

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.'

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

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.

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.

Many LLMs are proficient at generating code in various programming languages, debugging, explaining code snippets, and even translating code between languages. However, generated code always requires careful review.

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

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

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

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.

Content not available.

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

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.

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.

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

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

Comprehensive documentation for Claude AI, including prompt engineering tips, safety guidelines, and API usage examples.

Establish a protocol: 'After each response, I'll provide feedback. Use this feedback to improve your next response while maintaining the core requirements.'

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

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

Use LLMs to generate testable hypotheses based on your research questions and available data, helping guide your analysis direction.

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.

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.

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

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.

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

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.'

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

When building with AI, consider potential biases in training data, fairness of outcomes, transparency of decision-making, and the societal impact of your application.

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.

A collection of example code and guides for accomplishing common tasks with the OpenAI API, including best practices and optimization techniques.

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.

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

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

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

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.

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

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.

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.

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'.

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.

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.

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

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

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

Content not available.

Content not available.

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

Retrieval Augmented Generation (RAG) combines the knowledge of LLMs with specific document retrieval. To build a basic RAG system: 1) Process your documents into embeddings, 2) Store them in a vector database, 3) When queried, retrieve relevant content based on similarity, 4) Augment your prompt with this content, and 5) Generate contextually informed responses.

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

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.

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

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.

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.

The Turing Test, proposed by Alan Turing in 1950, tests a machine's ability to exhibit intelligent behavior indistinguishable from a human. Despite advances in AI, no system has conclusively passed a rigorous version of the test.

'Any sufficiently advanced technology is indistinguishable from magic.' - Arthur C. Clarke. This often feels true for modern AI capabilities.

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.'

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

The Turing Test, proposed by Alan Turing in 1950, tests a machine's ability to exhibit intelligent behavior indistinguishable from a human. Despite advances in AI, no system has conclusively passed a rigorous version of the test.