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When you need a specific output format, include a concrete example in your prompt rather than just describing it. Showing the model 'Output should look like: {name: string, score: number, summary: string}' with a filled example is far more effective than verbal descriptions alone.

Use few-shot examples in your prompts to dramatically improve output quality for specific formats.

For complex tasks, don't try to write the perfect prompt in one go. Start with a simple instruction, see the result, and then iteratively add more constraints, context, and examples to refine the output until it meets your requirements. This is more effective than starting with an overly complex prompt.

In-context learning is the ability of a large language model to learn a new task at inference time simply by being provided with a few examples in the prompt (few-shot prompting), without any updates to the model's weights. This is a powerful, emergent property of scale.

When asking an LLM to perform a task with a specific format, provide a few examples (2-3) in your prompt. This 'few-shot' approach helps the model understand the desired input-output pattern, leading to more consistent and accurate results.

Some tokenizers treat words with leading spaces differently. For example, ' hello' might be a different token than 'hello'. Be consistent with spacing, especially when providing examples, to ensure token efficiency.

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.

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 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.

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 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 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'.

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