The short version
Structured outputs constrain an AI response to a defined format, but a correctly shaped object can still describe the wrong customer, product or quantity. Before creating an order, validate its structure, resolve references against business records and apply the system's existing rules. Treat extracted values as proposals until those checks pass.
What are structured outputs?
Structured generation gives an application a predictable response format instead of requiring it to interpret arbitrary prose. Google's Gemini documentation describes generating responses against a supplied JSON Schema, with support limited to a subset of the specification. It also tells developers to validate values in the application.
That distinction matters when an AI feature moves from a demonstration to a business workflow. A field can contain an integer without representing the quantity the customer intended. A customer identifier can be a string without referring to an authorized account. Format constraints reduce one class of integration problem; the application still owns the decision to act.
JSON schema validation is the first gate
Define the fields the receiving application expects and how missing information is represented. JSON Schema distinguishes declaring a property's shape from requiring its presence. Its object reference also explains that extra properties are allowed by default unless the schema restricts them. Verify the subset supported by the selected model and API rather than assuming every schema keyword is enforced during generation.
Apply server-side validation before mapping the result into a business command. Distinguish malformed or incomplete responses from valid proposals that need clarification. Do not silently invent a missing value just to satisfy a required field. Where the generation API supports it, explicitly represent an unknown value; otherwise route incomplete extraction to a separate outcome.
Resolve the business meaning before writing
Illustrative example: a customer asks for two boxes of a part, but the existing order system stores quantities as individual units. The AI extracts a product description and the number two. Both fields may match the schema, while creating an order for two units would still be wrong.
Use the product catalogue to resolve the item and its packaging unit. Check the customer's permitted account, the delivery address and any applicable ordering restrictions through trusted application logic. If the source does not specify which packaging applies, ask for clarification rather than allowing the model to choose a convenient conversion.
Keep the original request reference with the proposal so a reviewer can trace an extracted value to its source. Do not let a generated customer ID determine authorization: derive the acting identity from the authenticated session or trusted integration context. Send a validated command through the existing business service, where current rules can be checked again at execution time.
Test rejected proposals as carefully as accepted ones
Build a small synthetic case set before enabling automatic order creation. Include an unknown product, two similar catalogue matches, a missing unit, an unauthorized customer reference and a response containing an unexpected field. For each case, specify whether the system should accept the proposal, request information or stop for review.
Record separate outcomes for extraction, structural validation, business validation and the final write. A successful model response is not an order reference. Confirm the result in the destination system, and keep unresolved proposals visible with an owner. Bound repair attempts so repeated regeneration does not hide missing business information or create unplanned operating cost.
When the schema changes, version the extraction contract and update its mapping and tests together. Check queued proposals produced under the previous contract before processing them with new assumptions. Measure rejection reasons and correction effort before expanding the workflow.
Make AI extraction part of maintained software
Start with one order path and a clear definition of what may proceed automatically. An experienced developer can use AI tools to build the adapter quickly, while retaining responsibility for the existing rules, tests and operating behavior. Additional domain or security expertise is still needed when the task demands it.
UVISION provides dedicated developers and managed projects with at least one full-time developer per project. For critical legacy systems, the work includes preserving business behavior, maintaining integrations and introducing AI as part of continued development. A useful first outcome is one intake flow whose accepted and rejected proposals can both be explained.
Common questions
What are structured outputs?
Structured outputs are AI responses constrained to a defined format, commonly described with a JSON Schema. They help an application process results consistently. A matching structure does not establish that a product, customer or quantity is correct. Before taking business action, the application must validate the proposal against trusted records, permissions and the rules of the receiving system.
What is JSON Schema validation?
JSON Schema validation checks whether data satisfies declared structural constraints, such as property types and required fields. A schema must explicitly express the restrictions the application needs. This check cannot by itself establish whether an extracted product matches the customer's request or whether the requester can act for an account. Those decisions require application logic and trusted business data.
How do you get structured output from an LLM?
Use a model and API that support a response schema, define the expected fields and verify which schema features are supported. Validate the returned data on the server before using it. Design an explicit path for missing information and rejected proposals, then test both accepted and rejected cases before allowing the output to trigger a business operation.
Further reading
Want to explore what this means for your systems?
Explore the relevant service