Vishal Uttam Mane is a Software Engineer, AI enthusiast, and tech author focused on backend development, scalable web applications, and artificial intelligence. He writes about software engineering&AI
This really resonates. The way you’ve framed fine-tuning vs. prompt engineering as a progression rather than a binary choice is spot on.
In my experience, starting with prompt engineering not only speeds up validation but also helps clarify what “good” actually looks like for the use case. That clarity makes any later fine-tuning far more targeted and effective.
The “Prompt Pyramid” idea is especially interesting, it reflects how iterative most real-world LLM work actually is. Too often people jump to fine-tuning before fully exploring what can be achieved with structured prompting.
Curious, have you seen cases where teams skipped fine-tuning altogether and still reached production-grade performance just through advanced prompting and tooling?
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This really resonates. The way you’ve framed fine-tuning vs. prompt engineering as a progression rather than a binary choice is spot on.
In my experience, starting with prompt engineering not only speeds up validation but also helps clarify what “good” actually looks like for the use case. That clarity makes any later fine-tuning far more targeted and effective.
The “Prompt Pyramid” idea is especially interesting, it reflects how iterative most real-world LLM work actually is. Too often people jump to fine-tuning before fully exploring what can be achieved with structured prompting.
Curious, have you seen cases where teams skipped fine-tuning altogether and still reached production-grade performance just through advanced prompting and tooling?