Neural Networks for Text: How to Measure and Optimize Quality
A Bitrix24 developer describes how he built an end-to-end eval system for Marta, the company's AI assistant: expert and synthetic datasets, and why retrieval metrics do not correlate with the real quality of answers. The write-up also covers how to close the agent loop so that every experiment is checked automatically rather than "by eye".
AI-processed from Habr AI; edited by Hamidun News
The Bitrix24 team has published a technical breakdown of a neural-network-based text processing system: how to measure the quality of the whole pipeline end to end, why retrieval metrics alone are not enough, and how to automate the experiment cycle.
Why are classic metrics not enough for a neural network?
Retrieval metrics (precision, recall, MRR) show how accurately the system finds the right documents, but they do not answer the key question: did the user get a useful answer?
How do you automate optimization of a neural network for text?
You need to automate the experiment cycle so that every change can be verified systematically. This makes it possible to measure not individual metrics but the quality of the entire information-processing pipeline.
Key Questions Answered
Why are classic metrics not enough for a neural network?
Retrieval metrics (precision, recall, MRR) show how accurately the system finds the right documents, but they do not answer the key question: did the user get a useful answer?
How do you automate optimization of a neural network for text?
You need to automate the experiment cycle so that every change can be verified systematically. This makes it possible to measure not individual metrics but the quality of the entire information-processing pipeline.
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