Prompt Engineering Best Practices

Effective Prompt Design: Strategies and Best Practices

The art of prompt design is pivotal in harnessing the full potential of AI models. A well-crafted prompt can be the difference between an average and an exceptional AI response. My team in Google have published guidelines for this that are well worth checking out in more detail

Prompt Engineering, visualized

Here are some key strategies and best practices to consider:

Clarity in Instructions:

Clear, concise instructions are crucial. They guide the AI to understand and respond accurately to the task at hand.

Inclusion of Examples:

Including examples in prompts can significantly enhance the model’s understanding and accuracy.

Partial Input Completion:

Designing prompts that allow the model to complete partial inputs can lead to more precise and relevant responses.

Specific Response Formats:

Prompting for responses in specific formats ensures consistency and relevance in the outputs.

Contextual Information:

Adding context to prompts helps in refining and focusing the model’s responses.

Effective Use of Prefixes:

Prefixes can guide the model in understanding the kind of response expected.


Trying different phrasings, parameter values, and prompt structures can lead to improved outcomes.

Iterative Design:

Continuous refinement and iteration of prompts are key to achieving optimal responses.

Fallback Responses:

Planning for fallback responses in case of inadequate or incorrect outputs is essential.

Caution with Factual Generation:

Be wary of over-reliance on the model for generating factual content, and exercise caution with tasks involving complex logic or mathematics.


These strategies, rooted in Google AI’s guidelines, are fundamental in crafting effective prompts that lead to high-quality, reliable AI outputs. For a more detailed exploration of these best practices, visit Google AI’s Prompt Design Strategies.