Master the art of rapid engineering

In the current world focused on AI, rapid engineering It is not only a fashionable word – it is an essential competence. This mixture of art and science goes beyond simple requests, allowing you to transform vague ideas into precise and exploitable IA inputs.

Whether you use Chatgpt 4o, Google Gemini 2.5 Flash or Claude Sonnet 4, four fundamental principles unlock the full potential of these powerful models. Master them and transform each interaction into a gateway to exceptional results.

Here are the essential pillars of effective fast engineering:

1. Master Clear and Specined Instructions

The basis of the content generated by high quality AI, including the code, is based on unambiguous guidelines. Tell it precisely what you want TO DO And how You want it to be presented.

For Chatgpt and Google Gemini:

Use strong action verbs: Start your prompts with direct orders such as “write”, “generate”, “create”, “convert” or “extract”.

Specify the output format: Explicitly indicate the desired structure (for example, “provide the code as a Python function”, “output in a JSON table”, “Use a numbered list for the steps”).

Define the scope and length: Clearly indicate if you need “a short script”, “a single function” or “code for a specific task”.

Promote example: “Write a python function called Calculation_rectangle_area which takes length and width as arguments and returns the area. Please include comments explaining each line.”

For Claude:

Use delimitors for more clarity: Enter your main instructions in separate tags like … or triple quotes (“” “…” “”). This segmentation helps compartmentalize and focus on the main task.

Use an affirmative language: Focus on what you to want The AI ​​to accomplish, rather than what you don't do it I want him to do.

Consider an “system prompt”: Before your main request, establish a character or a global rule (for example, “you are a developer Python Expert focused on clean and readable code.”).

Promote example: “” “Generate a Javascript function to reverse a chain. The function must be named \ Reversestring` and take an argument,` Inputstr`. “” “` `

2. Provide a complete context

AI models require relevant basic information to understand the nuances of your request and prevent erroneous errors, anchoring their answers in your specific scenario.

For Chatgpt and Google Gemini:

Include substantive details: Describe the scenario or the code goal (for example, “I build a simple web page, and I need JavaScript for a button click.”).

Define the variables / data structures: If your code must interact with specific data, clearly describe its format (for example, “entry will be a list of dictionaries, where each dictionary has” name “and” age “” keys).

Mention the dependencies / libraries (if known): “Use the API call library.”

Promote example: “I have a CSV file called Products.csv with columns ‘Item', ‘Price' and ‘Quantity'. Write a Python script to read this CSV and calculate the total value of all the elements (price * quantity).”

For Claude:

Clearly segment context: Use separate sections or delimitors to introduce general information (for example, …).

Define a character: As indicated, establish a specific role for Claude in the prompt (for example, “you act as a senior frontal developer”) immediately frames his response in this expertise, influencing tone and depth.

Promote example: I develop a small React application. I need a component that displays a welcome message. Create a functional react component named \ Welcomemage ‘which accepts an “ accessory and displays “Hello, (name)!”. “

3. Use illustrative examples (a few strokes)

Examples are incredibly powerful educational tools for LLM, especially when they demonstrate desired models or complex transformations which are difficult to articulate only by a descriptive language.

For all LLM (Chatgpt, Gemini, Claude):

Show the entrance and exit exit: For a function, clearly demonstrate its planned behavior with specific inputs and their corresponding correct outputs.

Provide examples of formatting: If you need a specific output style (for example, a precise JSON structure), include a sample of this format.

Invitation “to a few blows”: Incorporate 1 to 3 example input pairs and their respective desired exit. This guides AI in understanding the underlying logic.

Example of a prompt (for all LLM): “Write a Python function that converts Celsius temperatures to Fahrenheit. Here is an example:

Entrance: Celsius_to_fahrenheit (0)

Exit: 32.0

Entrance: Celsius_to_fahrenheit (25)

Outing: 77.0 ″

4. Adopt an iterative and experimental approach

Rarely is the perfect prompt designed during the first attempt. Expect to refine and iterate according to the initial AI responses to obtain optimal results.

For Chatgpt and Google Gemini:

Provide error messages for debugging: If the code generated does not run, glue the exact error message in the cat and ask the AI ​​to debug or explain the problem.

Describe the unexpected outing: If the code runs but produces an incorrect or unwanted result, clearly explain what you have observed in relation to what you expected.

Ask for alternatives: Invite with questions like “can you show me another way to do it?” Or “Can you optimize this code for speed?”

For Claude:

Clarify and add new constraints: If the output is too wide or lacks a specific detail, introduce a new instruction (for example, “please make sure that the code manages negative inputs.”)

Refine the character: If the tone or style of the content generated is not quite correct, adjust the prompt of the initial system or add a specific instruction as “adopt a more concise coding style”.

Decompose complex tasks: If Claude is struggling with large multifaceted demand, simplify it in smaller and manageable steps and ask for code for each step individually.

By systematically applying these principles and including the subtle preferences of different LLM, you can transform your AI into an incredibly effective coding assistant, rationalize your projects and widen your problem solving capacities.