Prompt Engineering: Techniques for Getting Better Responses from AI Models
Introduction
With the advancement of natural language-based artificial intelligence models, it has become essential to know how to interact efficiently with these technologies. One of the main concepts in this context is the "prompt." A prompt is the term used to describe the instruction, command, or question sent to an AI model to obtain a specific type of response. The quality and clarity of this prompt directly influence the accuracy, relevance, and usefulness of the answer generated by the model.
The process of creating, adjusting, and combining prompts using specific techniques is known as prompt engineering. The goal is to ensure that the instructions sent to the model are correctly interpreted, maximizing the potential of AI in tasks such as text generation, question answering, process automation, and much more.
Below are some of the main prompt engineering techniques. It is not necessary to use all of them at once; they can be refined or combined according to the desired objective.
Objective and Clear Instructions
Instructions should be direct, specific, and detailed about what is expected from the model. The clearer the request, the greater the chance of obtaining results aligned with the task’s goal. This approach helps the model understand the demand from the outset.
Instruction Repetition
For more complex requests, it is useful to reinforce the request at the end of the prompt. Repeating instructions or important points increases the chances that the model will fully meet expectations, avoiding omissions or misinterpretations.
Guardrails (Safety Barriers)
This technique consists of adding explicit limitations to the response to avoid harmful, irrelevant, or incorrect content and to align the model’s output with ethical standards. Guardrails can block responses related to copyright violations, security issues, or other sensitive topics, making the use of AI safer and more responsible.
Example: Make sure the proposal is practical and simple to implement for a basic game. Avoid complex or unrealistic mechanics.
Prepare the Output
Defining words, phrases, or instructions at the end of the prompt can help shape the format of the response, making it more organized and understandable. This facilitates the reading and use of the information provided by the AI.
Example: Organize the answer into bullet points or lists.
Chain of Thought Request
Explicitly requesting the model to explain the reasoning step by step until reaching the final conclusion increases the transparency and quality of the responses. This technique is useful for tasks that require detailed analysis or justifications.
Example: Perform the step by step “pass the instruction.”
Specify Output Structure
Indicate the exact format in which the response should be generated, such as JSON, lists, bullet points, or tables. This helps obtain organized results, making it easier to analyze and integrate with other systems or processes.
Example: Your instruction must use the Java framework with Spring Boot, tests should be done with Junit and with the Assertions library for validations. Follow the provided output structure.
Clear and Visual Syntax
Organize the prompt using formatting like lists, headings, bullet points, or tables. Making the instructions visually organized helps the model understand and interpret the information more efficiently.
Example: Format the game proposal in a visual table for quick presentation.
Conclusion
Prompt engineering is an essential skill for anyone who wants to make the most of AI models’ potential. By mastering these techniques and applying them according to the context, it is possible to obtain more relevant, safe, and goal-aligned responses.
