ML engineer shifts from custom model training to LLM prompt optimization

By PulseAugur Editorial · [1 source] · 2026-05-25 15:50

An ML engineer specializing in NLP and audio is shifting focus from training custom models to optimizing prompts for large language models. While they miss building models from scratch, the current work with LLMs presents new, challenging problems, particularly in evaluating text outputs where even human judgment is difficult. AI

IMPACT Reflects a shift in ML engineering focus towards prompt engineering over custom model development.

RANK_REASON The item is a personal reflection on a trend in ML engineering, not a primary source announcement.

Read on Mastodon — mastodon.social →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

COVERAGE [1]

Mastodon — mastodon.social TIER_1 · msvana · 2026-05-25 15:50

I work as an ML engineer (NLP and audio). Unsurprisingly, we are moving away from training custom models to finding a good prompt for an LLM. I sometimes miss b

I work as an ML engineer (NLP and audio). Unsurprisingly, we are moving away from training custom models to finding a good prompt for an LLM. I sometimes miss building new models from scratch. But the use of LLMs brings its own challenges that make the work fun. I am currently sp…

COVERAGE [1]

I work as an ML engineer (NLP and audio). Unsurprisingly, we are moving away from training custom models to finding a good prompt for an LLM. I sometimes miss b

RELATED ENTITIES

RELATED TOPICS