Foundation models are powerful, but out‑of‑the‑box they’re rarely production‑ready. Fine‑tuning is what helps align AI systems with real business needs, safety expectations, and quality standards.
From a QA engineer’s perspective, fine‑tuning—through approaches like instruction tuning and RLHF—is critical for improving reliability, consistency, and trust in AI outputs.
I’ve shared a deeper breakdown of:

what fine‑tuning really means
why it’s needed for production systems
how QA principles apply to data preparation and evaluation

Read the full post here:
https://hemaai.hashnode.dev/fine-tuning-isn-t-optional-how-qa-engineers-make-ai-models-production-ready
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