Prompt Engineering
Prompts are the instructions we give to the LLM to get a response. We will look at three main tips: the first is to be detailed and specific, the second is to guide the model to think about its response, and the third is to experiment and iterate.
If we ask the model to draft an email promoting our website, it will give us a generic response. But if we provide additional context, such as names, website links, and the company name, the LLM will have a more relevant context to compose the email. We can also guide the model to think about its response.
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For example, if we ask it to write a code to create a Fibonacci sequence, it could do it quite well. But if we want it to explain the process step by step, we could prompt it like this: "Explain step by step how to generate the Fibonacci sequence in Python."
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With a prompt like this, we could obtain a detailed and comprehensible result.
Finally, experiment and iterate. You could start with "Help me review this text," and if you don't like the outcome, you could try again. If you still don't get exactly what you want, you could start over. Very often, the prompting process is not about starting with the right prompt but about starting with something and then adjusting the prompt to get closer to the desired response.
It's about starting with something and then reviewing if the results are satisfactory and knowing how to adjust the prompt to get closer to the desired response.
Response Control
This refers to the techniques and strategies used to control and structure a model's responses. It's important to ensure that the responses are useful and accurate.
For example, if we want the model to provide responses that involve tables or data in a JSON format, we can request structured data to guide its text generation.
We can also specify the length of the response, whether it's a certain number of words or a specific type of content, including the tone, be it formal or informal. To ensure effective communication, we can modify the style as well. Additionally, we can identify safety and ethical conditions, applying filters and controls to prevent the generation of inappropriate content.
It's necessary to implement rules to avoid responses that include offensive language, false information, or biased content. Further, it might be necessary to customize the context, tailoring the responses to make them more relevant and personalized to the given context.
Predefined templates can be used for the model to fill in relevant information. For instance, a template for text-based summaries or programming code snippets with a structured format.
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This cycle ensures that the project is effectively managed, meeting all objectives and quality standards. It's also important to deploy and evaluate the iterative process.
It's crucial to define the project's objective, ensuring it aligns with the agent's goals, clearly establishing requirements and targets. Build the system's flow and verify which parts of the system need to be included or have already been included.
Testing is essential to ensure the system functions correctly and meets all standards. Deploy and continuously monitor the responses to detect any issues promptly.
