When Prompts Start to Learn: Rewriting the Rulebook of LLM Tuning

Author(s): R. Thompson (PhD)

Originally published on Towards AI.

From rigid instructions to living feedback loops that grow smarter with every critique, one line of English at a time

Nine months ago, our team shipped a shiny prototype that promised to turn plain spreadsheets into full‑blown marketing pages. The day it hit production, we watched in slack‑jawed silence as the LLM forgot brand colours, dropped price tags, and — most baffling of all — replaced the client logo with 💩. A thousand‑line prompt that had sailed through QA broke under the weight of real users punching keys in ways we had never imagined. My first instinct was to hard‑code more examples. My second was to blame the model. My third, which arrived after too many 2 a.m. bug‑hunts, was a heretical thought:

This article discusses the evolution of prompt design through the development of a system that learns and adapts prompts in real-time based on user interactions and feedback. It emphasizes the importance of dynamic prompts rather than static ones, detailing the underlying mechanisms and real-world applications, such as enhancing UX in various tools and addressing user feedback for improvement. The author shares insights from multiple case studies to illustrate the efficiency and necessity of a self-learning approach in prompt management, ultimately advocating for a shift towards a more responsive framework in machine learning applications.

Read the full blog for free on Medium.

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Published via Towards AI