How to Build Reliable Multi-Label Text Classification in Node.js Without Taxonomy Drift

Developers building catalog classification systems with large language models face a key risk: syntactically valid JSON output can still contain outdated, duplicate, or mismatched taxonomy labels that are harder to detect than outright parse errors. A robust Node.js architecture separates concerns into four records — normalized product text, a taxonomy snapshot, the raw model response, and the validated classification — ensuring the model's output is treated as a proposal, not a final result. Exactness in multi-label classification requires that responses contain only one JSON object, use declared label strings, and match the specific taxonomy version tied to each request. To prevent duplicate or conflicting published results, an operation key built from the product ID, text digest, taxonomy version, and policy version enforces an exactly-once commit boundary even under at-least-once delivery conditions. For large taxonomies, candidate retrieval via embeddings can narrow the label set, but omitted candidates cannot be recovered later, making retrieval-policy versioning essential for auditability.