Store Prompt and Response for Distillation?
The question surfaced in the LocalLLaMA community on March 23rd: should you be systematically storing your prompts and model responses specifically to use them later for knowledge distillation? The framing is practical rather than theoretical. Someone, somewhere in the practitioner community, is either already doing this or seriously considering it — and thought the question worth putting to the crowd. The score of 4.7 suggests a modest but genuine level of engagement, the kind of thread that attracts people who've bumped into exactly this problem themselves. The core idea is straightforward: if a larger, more capable model is generating responses you're already using, those prompt-response pairs are a latent training asset. Log them now, distill later.
If confirmed as an emerging practice, here is what this means. Small teams running inference on capable models — GPT-4-class, Claude, the frontier open-weights releases — are sitting on training data they're currently discarding. Systematic logging flips that waste into a flywheel: proprietary fine-tuning data generated from your own use cases, shaped by your own prompts, at near-zero marginal cost. The second-order effect is significant. It democratises a technique that large labs use deliberately — distilling smaller, faster, cheaper models from larger ones — and puts it within reach of anyone with decent logging infrastructure. The strategic implication is that organisations which start capturing this data now build a compounding advantage over those who don't. It's not dramatic. It's just the difference between a training set and an empty folder.
Watch for whether tooling projects — logging frameworks, LLM observability platforms — begin explicitly advertising distillation pipelines as a feature. That would signal the practice moving from community conversation to standard workflow.
<!-- SC_OFF --><div class="md"><p>Hi there!</p> <p>My question(-s) are at the bottom, but let me tell you what I am trying to do and how, first:</p> <p>For my work-in-progress
<!-- SC_OFF --><div class="md"><p>I ask that because I was thinking about if that may be valuable in the future once llms improve more. </p> <p>Let’s imagine a perfect future
<!-- SC_OFF --><div class="md"><p>Drowning in research papers — what AI tools help you make sense of clinical / research data?</p> <p>I work in healthcare space and regularly need
<!-- SC_OFF --><div class="md"><p>Model size gets a lot of attention. I believe the real difference between a frustrating and a great local LLM experience comes from everything
<!-- SC_OFF --><div class="md"><p>So it’s no secret that chinese labs use american models for sft data and hey that’s completely fine with me I have no remorse for anthropic but
<table> <tr><td> <a href="https://www.reddit.com/r/OpenAI/comments/1thln7j/remove_the_assumedhuman_layer_from_prompting/"> <img alt="Remove the assumed-human layer from prompting"
<!-- SC_OFF --><div class="md"><p>So I'm looking in these files and I see a huge dump. It seems like there's the entire base instruction. Is this already open source or am I
<!-- SC_OFF --><div class="md"><p>I actually make some tests with lm studio and Opencode with the new Gemma 4 26b model.</p> <p>The results are really impressive especially on
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