Prompt engineering refers to the practice of optimizing textual input to a large language model (LLM) to improve output and receive the responses you want. Prompting helps an LLM perform a wide variety of tasks, including classification, question answering, code generation, creative writing, and more. The quality of prompts that you provide to a LLM can impact the quality of the model's responses. This section provides you the necessary information to get started with prompt engineering. It also covers tools to help you find the best possible prompt format for your use case when using a LLM on Amazon Bedrock.
The effectiveness of prompts is contingent upon the quality of the information provided and the craftsmanship of the prompt itself. Prompts may encompass instructions, questions, contextual details, inputs, and examples to effectively guide the model and enhance the quality of the results. This document outlines strategies and tactics for optimizing the performance of Amazon Nova Family of Models. The methods presented herein may be employed in various combinations to amplify their effectiveness. We encourage users to engage in experimentation to identify the approaches most suitable for their specific needs.
Before you start prompt engineering, we recommended you have the following elements in place, so you can iteratively develop the most optimal prompt for your use case:
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Define your use case: Define your use case you want to achieve on 4 dimensions
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What is the Task - Define the task you want to accomplish from the model
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Whats the Role - Define the role that the model should assume to accomplish that task
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Whats the Response Style - Define the response structure or style that should be followed based on the consumer of the output.
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What set of Instructions to be followed: Define the set of instructions that the model should follow to respond as per the success criteria
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Success Criteria: Clearly define the success criteria or evaluation criteria. This can be in the form of a list of bullet points or as specific as some evaluation metrics (Eg: Length checks, BLEU Score, Rouge, Format, Factuality, Faithfulness).
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Draft Prompt: Finally, a draft prompt is necessary to initiate the iterative process of prompt engineering.
The Amazon Nova model family consists of two broad model categories, understanding models (Amazon Nova Micro, Lite, and Pro) and content generation models (Amazon Nova Canvas and Reel). The following guidance addresses the text understanding model and the vision understanding models. For guidance on image generation prompting, see Amazon Nova Canvas prompting best practices and for guidance on video generation prompting, see Amazon Nova Reel prompting best practices.
