Testing the behaviour of generative AI

A product that uses language models for content creation and profile design needed confidence in its AI output. Model answers vary, so conventional pass/fail tests weren't enough.

Built an evaluation approach that combines automated output checks, NLP analysis (spaCy, NLTK) and manual bias review, plus integration tests for response storage and context retention, alongside monitoring of the third-party model's health.

A repeatable way to find output and integration defects, with findings that fed back into model settings and product quality.