This article outlines eleven essential practices for effective prompt engineering in production environments, emphasizing the need for robust, repeatable methods over one-off tricks. Key recommendations include being specific and unambiguous, using examples to demonstrate desired behavior, and providing only necessary context. The guide also stresses explicit output format specification, separating instructions from data, and starting with simple prompts before adding complexity. It advises defining behavior for uncertain situations, versioning prompts as code, and rigorously evaluating prompts before and after deployment. Finally, it highlights the critical importance of treating all model inputs as untrusted to prevent prompt injection attacks. AI
IMPACT Provides actionable guidance for developers to improve the reliability and safety of LLM integrations in production applications.
RANK_REASON The article provides practical advice and techniques for using LLMs in a product, rather than announcing a new model or research.
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