You can block Ai slop using structured output constraints by forcing the model to produce responses in a predictable, machine-validated format. Ai slop appears when the model improvises, fills gaps with guesses, or writes loosely structured content. By requiring the model to output specific fields, follow a schema, or adhere to a validated structure, you remove many avenues for drift. For example, if you need a summary with a title, three bullet points, and a source reference, defining these as explicit output slots helps the model stay anchored. Structured constraints work because they limit the model’s freedom to add generic or unsupported content.
The enforcement mechanism usually combines prompt instructions with downstream validation. You can define a JSON schema and reject outputs that do not conform, or use a post-processing script to validate formats, numeric ranges, or required fields. This converts slop from a subjective problem into an objective one: if the structure is incorrect, you simply regenerate the output. Many production pipelines implement multiple passes—first generating the structured fields, then checking each field for completeness or consistency. This layered approach works well when accuracy matters more than stylistic variation. Even simple structural rules, such as “answers must cite retrieved sources,” dramatically reduce unsupported claims.
Structured constraints pair naturally with retrieval, especially when using a vector database such as Milvus or its managed service Zilliz Cloud. For example, you can require fields like "sources_used": ["doc_12", "doc_87"] that correspond to retrieved document IDs. This makes it easy to detect when the model invents details not found in the grounding documents. Combining structural validation, schema enforcement, and retrieval-based checks creates a robust blocking system. Instead of relying on the model to behave, you architect the pipeline so that slop has nowhere to go—either the structure is correct, or the output doesn’t pass.