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In modern application development, integrating large language models (LLMs) requires them to return structured data, not just free text. To achieve this, there are various strategies that allow shaping the output of an LLM according to the project's needs. Below are five practical approaches, each with its advantages and ideal contexts, which we commonly apply at Q2BSTUDIO when building custom applications with artificial intelligence.
The first method consists of defining typed schemas using libraries like Zod. This technique is ideal when a strict contract is required to validate types, properties, and relationships between fields. By using it, the LLM generates data that exactly fits the defined structure, reducing runtime errors. It is especially useful in custom software projects where data consistency is critical, such as management systems or analytics platforms.
A second approach is to use JSON Schema directly, which is very convenient when definitions generated by other ecosystem tools are already available. This option avoids having to rewrite schemas in a different format, saving time and maintaining compatibility with legacy systems. At Q2BSTUDIO we integrate it with our AWS and Azure cloud services, ensuring data flows seamlessly between components.
For simpler cases, such as extracting a single piece of data in a specific format (a date, an identifier), defining a regular expression pattern is sufficient. This approach is fast, lightweight, and does not require complex structures. It is very effective in automation processes and in building AI agents that need precise and concise responses.
When validation depends on business rules that cannot be easily expressed with a type, a custom validation function can be used. This function receives the model's output and decides whether it meets the logical conditions (for example, “if field A is X, then B must be Y”). This method offers maximum flexibility and is used in advanced AI for business solutions where rules are dynamic.
Finally, for environments that work with hierarchical data in XML, such as accounting or billing systems, the best alternative is to use XML templates with typed placeholders. The model fills in the values within the predefined structure, keeping the markup intact. This technique is essential in integrations with business intelligence platforms or power bi, where reports and dashboards require specific formats. At Q2BSTUDIO we incorporate these mechanisms with a focus on cybersecurity, ensuring sensitive data is processed correctly.
Choosing the right option depends on the context: for critical applications, strict schemas are recommended; for rapid prototypes, simple patterns. Our experience in developing custom applications leads us to combine these techniques, thereby optimizing workflows with LLMs and ensuring reliable results.
