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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.

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.

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.

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.

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.

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.

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

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.'

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

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

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

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.

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

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.

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

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'.

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

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

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

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

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.

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

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.

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.

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.

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

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