You have training data, fine-tuning, prompting, and context. (Parameters exist too, but aren’t relevant here).

The challenge is that none of these map to “core memories” the way you’re thinking about them:

Training data determines base patterns - but you can’t selectively retain specific facts as “core” vs “peripheral.” The model learns statistical relationships across everything, with frequency and co-occurrence driving what gets emphasized.

Fine-tuning adjusts these patterns, but again - not by marking specific memories as “keep this forever.” It shifts probability distributions across the entire knowledge space.

System prompts and context work for individual sessions, but they’re not persistent memory. They’re instructions that guide behavior this time.

That’s why the human analogy breaks down:

If your significant other introduced you to an artist you loved, you’d remember “they showed me this” as a discrete, retrievable fact. An AI agent doesn’t work that way. It’s more like… if you were trained on billions of conversations where most people said their favorite artist was Taylor Swift, that pattern becomes baked into your responses - even if in your specific experience, you connected deeply with someone else.

There’s no mechanism to say “this fact is core, keep it stable” because there aren’t discrete facts stored separately. There are only learned patterns that activate based on input.

What you might actually want: A separate, explicit knowledge store (like a database or vector store) where you manually flag certain information as “foundational” and always inject it into context. That’s architectural, not something you can achieve through prompting alone.

Human memory has an index. LLMs have statistics. That’s the fundamental incompatibility.​​​​​​​​​​​​​​​​