Assemble each RAG prompt from a base plus rules per question

In the current generative artificial intelligence ecosystem, RAG (Retrieval-Augmented Generation) systems have become the cornerstone of many enterprise solutions. However, a recurring challenge is how to maintain consistency and quality of responses when each query may require different nuances. Instead of building a unique prompt for each question, a more robust approach consists of assembling each prompt from a common base —which defines the model's general behavior, tone, and security constraints— and adding specific rules per question, such as the output format, sources to prioritize, or the depth of analysis. This modular architecture allows scaling the implementation of AI for businesses without losing control over quality. An orchestrator (or dispatcher) is responsible for receiving the parsed question, identifying the query type, and dynamically combining the base with the corresponding rules, thus generating a typed prompt that is sent to the LLM. This pattern not only improves accuracy but also facilitates auditing and fine-tuning of each component. The practical implementation of this model requires a solid infrastructure, either through artificial intelligence services that integrate AI agents and data pipelines, or by relying on AWS and Azure cloud services to ensure scalability and availability. Additionally, customizing these systems often involves developing custom applications and custom software that adapt to specific business flows, incorporating cybersecurity layers to protect both sensitive data and the models themselves. On the other hand, integration with business intelligence service tools such as Power BI allows visualizing prompt performance and detecting improvement patterns. Ultimately, assembling each RAG prompt from a base plus rules per question is a strategy that every technical team should consider, and having a partner like Q2BSTUDIO, specialized in software development and emerging technologies, accelerates the adoption of these practices with quality guarantees and full customization.