LLMs stay blank, you absorb the learning
Models don't retain context between sessions—the burden of prompting skill accumulates entirely on you, not the tool.
May 19, 2026
Summary
Understanding that LLMs are stateless changes how you architect workflows. You're not building persistent agent memory; you're optimizing your own prompt patterns and context-passing strategy.
Why it matters
Understanding that LLMs are stateless changes how you architect workflows. You're not building persistent agent memory; you're optimizing your own prompt patterns and context-passing strategy.
Implementation verdict
This replaces the assumption that fine-tuning or continued learning happens server-side. It requires you to externalize everything—store conversation context, version your prompt templates, treat the model as a pure stateless function. Worth acknowledging now before you design for the wrong mental model.
Sources
- 1.An LLM doesn't work that way. It learns nothing about me between sessions.
- 2.The chisel didn't change. I did.
- 3.You become the patina.
- 4.The tool is patient and unchanged.
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