In the fascinating field of artificial intelligence, an important discipline is emerging: “Prompt Engineering“.

In a time where machines possess the ability to understand and generate text and code, focusing on prompt engineering becomes crucial to enhance the effectiveness and significance of interactions between humans and AI models specifically Large Language Models (LLMs).

While many rely on traditional methods for code generation, we have chosen to explore a more innovative approach: “Model-Driven Prompt Engineering (MDPE)“.

What is Prompt Engineering and Why is it Important?

Prompt engineering involves creating precise instructions (prompts) that guide an AI model to generate desired outputs for specific tasks. Imagine an architect whose precise pencil strokes ensure the majestic construction of a building. Similarly, prompt engineering ensures that LLMs produce code with unmatched efficiency and relevance. Excellence in this field lies not in merely following traditional methods but in innovation.

What is Model-Driven Prompt Engineering (MDPE)?

Model-Driven Prompt Engineering (MDPE) is an advanced approach that constructs prompts methodically from predefined software model such as a class diagram, ensuring they are structured and guided by models and meta-models to optimize code generation through LLMs.

How Does Model-Driven Prompt Engineering (MDPE) Enhance Code Generation?

Model Driven Prompt Engineering (MDPE) uses models and meta-models to structure and guide code generation through Language Model Models (LLMs). Here is an explanation of each step of the process:

1. Model Analysis: Before designing prompts, it is essential to deeply understand the model and extract all key concept identifications by defining the main entities (Classes, Objects, Components). Also, identify the attributes, properties, and relationships of each entity. This includes defining integrity constraints and validation rules.
2. Prompt Construction: Based on this analysis and by adding complementary elements such as project documentation and sometimes significant prompts from the user, structured and well-defined prompts are created. For example, instead of simply asking “Write a function to sort an array,” an MDPE prompt could specify “Write a Python function that sorts an array of integers using the quicksort algorithm.”
3. Interrogation of Language Model Models (LLMs): Use the constructed prompts to query the language models and obtain generated code that conforms to the models and meta-models.

Why Should We Use MDPE in Code Generation?

The importance of this approach is immense. The ability to generate code quickly and correctly is crucial. The Model Driven Prompt Engineering transforms this ability into a mastered art, where each interaction with AI is finely orchestrated to produce innovative solutions perfectly tailored to developers’ needs.

Model-Driven engineering plays a significant role in prompt construction. They enable:

1. Improved Accuracy: Models help design prompts that guide the model more precisely. By providing clearer, more detailed, and well-structured instructions that specifically address the need.
2. Error Reduction: Reducing the number of errors and inconsistencies in the generated code.
3. Time Savings: Building good and efficient prompts accelerates the code generation process.

Use Case

We have conducted several experiments across various use cases, including prominent examples using JMS and RabbitMQ connectors. We have also tested on various internal projects of the company, such as BL.MOM [BL.MOM Connector Generation – Part 2 – BL Research (research-bl.com)], one of the projects used in our communication system. Regarding the language models (LLM) used, we have tested several LLMs, but we have decided to use Llama3 and Phi3 for code generation. Also, below are the results obtained in the experiments on the RabbitMQ Publisher/Subscriber example [RabbitMQ tutorial – Publish/Subscribe | RabbitMQ]:

Figure : Messaging System: Publisher-Subscriber Powered by RabbitMQ

Prompt: The prompt contains all the information necessary to define the class “Subscriber” for this example:

Result:

Conclusion

Model Driven Prompt Engineering in code generation is a structured process that relies on a deep understanding of models and meta-models. By carefully analyzing these structures and building detailed and aligned prompts, it is possible to effectively guide language models to produce accurate, relevant, and specification-compliant code. This methodical approach helps optimize the relevance and accuracy of the code produced by advanced language models.