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Use specific, detailed prompts to get more accurate responses from LLMs and reduce token costs.

To get structured data, explicitly ask the LLM to format its response as JSON, a markdown table, or a numbered list. For example: 'Compare the top 3 smartphones and provide the output in a markdown table with columns for Model, Price, and Key Feature.'

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.

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.'

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.

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.'

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.

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.

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.

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.

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.'

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

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.'

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.

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

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'.

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.

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?'

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

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.

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.

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

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.

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).

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.'

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.'

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