Boost Your Business with GenAI and GCP: Simple and for Everyone

As the examples are very specific to particular technologies, any change in the prompt in any example can affect the results, or also modifying any word in the input request. This means that the prompt is not robust enough to assimilate different expressions without affecting the generated code. To have a productive prompt and system, more training and different variety of solutions, requests, expressions, etc., are needed. With all this, we will finally be able to have a first version to present to our business user so that they can be autonomous.Specifying the maximum possible detail to an LLM is crucial for obtaining precise and contextual results. Here are several tips to keep in mind to achieve appropriate results:

Clarity and Conciseness:

• Be clear and concise in your prompt, avoiding long and complicated sentences.
• Clearly define the problem or task you want the model to address.

Specificity:

• Provide specific details about what you are looking for. The more precise you are, the better results you will get.

Variability and Diversity:

• Consider including different types of examples or cases to assess the model's ability to handle variability.

Iterative Feedback:

• If possible, iterate on your prompt based on the results obtained and the model's feedback.

Testing and Adjustment:

• Before using the prompt extensively, test it with examples and adjust as needed to achieve desired results.