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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.

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.

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.

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.

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.

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.'

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.

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

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.

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.'

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.

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.

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.

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

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

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

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

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

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

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.

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.

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.

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

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.

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.

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.'