LLM Text Classification: 4 JSON Schema Invariants for Reliable CRM Tags
Use JSON Schema when an LLM's classification result becomes a CRM action. Keep prompt parsing for disposable analysis, prototypes, and human-reviewed notes. The distinction is not about making the model “understand JSON.” It is about placing a checkable contract between uncertain text generation and a system that will create a task, change a field, or trigger an escalation. For a logistics sales…
JSON Schema provides a reliable way to structure the output of a language model (LLM) for integration with a Customer Relationship Management (CRM) system. By defining a JSON Schema with specific invariants, the LLM's classification result can be checked for reliability before it becomes a CRM action. This ensures that the output adheres to a set of rules, such as having a closed set of labels, appropriate value types, required fields, and explicit representation for uncertainty.
When processing a transcript of customer interactions, such as a logistics sales call, four invariants are recommended before accepting the LLM's result. These include:
1. A closed set of labels: The intent should be chosen from a finite set of predefined options.
2. Correct value types: The confidence score should be a number between 0 and 1, and the follow_up_required field should be a boolean value.
3. Required fields: Certain fields, like the follow_up_required flag, must be present in the output.
4. Explicit representation for uncertainty: The confidence score and evidence should be provided alongside the intent.
By validating the response against these invariants, malformed or out-of-contract classifications can be easily identified and handled appropriately. For instance, if the response contains a service_issue label followed by a quote_request label, the schema can reject the array when a single intent is expected. This helps to prevent hidden domain decisions from being made silently by a parser based on delimiter parsing, which is a weak boundary compared to a JSON Schema-based approach.
JSON parsing alone only checks if the characters can be decoded as JSON, but it does not validate the correctness or format of the individual fields. On the other hand, a JSON Schema defines the exact structure and constraints of the expected output, making it a more robust solution for ensuring reliable CRM tags.
When implementing this approach, a JSON Schema can be defined, such as the one shown in the provided source. The schema specifies the expected structure and constraints for the classification output, including the allowed values for the intent field (as a closed set of labels), the appropriate range for the confidence score, and the requirement for the presence of certain fields like follow_up_required. The schema also specifies that the evidence field must be a non-empty string.
A function, such as classify_call, can be defined to handle the LLM's response. This function would take the transcript as input, perform the necessary validation using the JSON Schema, and return the validated classification as a dictionary. If the validation fails, the function should reject the record and quarantine the failure for review.
By enforcing these four invariants through a JSON Schema and performing validation before any CRM write operation, the failure is contained within the system. The original model response can still be retained for diagnosis purposes, ensuring that the deterministic consumer (the CRM system) only receives well-formed and contract-compliant data.
This approach prevents the hidden schema created by delimiter-based parsing from taking over, providing a clear boundary between the probabilistic model output and the deterministic application consumer.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
