Building a self-learning feedback loop for our classification engine.
This system leverages human feedback through a hybrid approach:
- Users flag incorrect classifications
- Pattern matching suggests corrections instantly and at no cost
- A large language model (LLM) provides deeper suggestions when necessary
- An admin reviews and approves the suggestions
- The system improves automatically
This method combines the efficiency of fast pattern matching, which addresses 80% of cases, with the LLM's capability to handle complex edge cases. Every correction is directly integrated into our database, enhancing the system's intelligence over time.
There is no need for manual retraining or data silos, just a commitment to continuous improvement.
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