You delete a dependency. ChatGPT acknowledges it. Five responses later, it hallucinates that same deprecated library into your code. You correct it again—it nods, apologizes—and does it once more.

This isn’t just an annoying bug. It’s a symptom of a deeper problem: LLM applications don’t know what to forget.

Developers assume generative AI-powered tools are improving dynamically—learning from mistakes, refining their knowledge, adapting. But that’s not how it works. Large language models (LLMs) are stateless by design. Each request is processed in isolation unless an external system supplies prior context.

That means “memory” isn’t actually built into the model—it’s layered on top, often imperfectly. If you’ve used ChatGPT for any length of time, you’ve probably noticed:

  • t remembers some things between sessions but forgets others entirely.
  • It fixates on outdated assumptions, even after you’ve corrected it multiple times.
  • It sometimes “forgets” within a session, dropping key details.

These aren’t failures of the model—they’re failures of memory management.

How memory works in LLM applications

LLMs don’t have persistent memory. What feels like “memory” is actually context reconstruction, where relevant history is manually reloaded into each request. In practice, an application like ChatGPT layers multiple memory components on top of the core model:

  • Context window: Each session retains a rolling buffer of past messages. GPT-4o supports up to 128K tokens, while other models have their own limits (e.g. Claude supports 200K tokens).
  • Long-term memory: Some high-level details persist across sessions, but retention is inconsistent.
  • System messages: Invisible prompts shape the model’s responses. Long-term memory is often passed into a session this way.
  • Execution context: Temporary state, such as Python variables, exists only until the session resets.

Without external memory scaffolding, LLM applications remain stateless. Every API call is independent, meaning prior interactions must be explicitly reloaded for continuity.

Why LLMs are stateless by default

In API-based LLM integrations, models don’t retain any memory between requests. Unless you manually pass prior messages, each prompt is interpreted in isolation. Here’s a simple example of an API call to OpenAI’s GPT-4o:

When LLM applications won’t let go

Some LLM applications have the opposite problem—not forgetting too much, but remembering the wrong things. Have you ever told ChatGPT to “ignore that last part,” only for it to bring it up later anyway? That’s what I call “traumatic memory”—when an LLM stubbornly holds onto outdated or irrelevant details, actively degrading its usefulness.

For example, I once tested a Python library for a project, found it wasn’t useful, and told ChatGPT I had removed it. It acknowledged this—then continued suggesting code snippets using that same deprecated library. This isn’t an AI hallucination issue. It’s bad memory retrieval.

GenAI memory must get smarter, not bigger

Simply increasing context window sizes won’t fix the memory problem. LLM applications need:

  • Selective retention: Store only high-relevance knowledge, not entire transcripts.
  • Attentional retrieval: Prioritize important details while fading old, irrelevant ones.
  • Forgetting mechanisms: Outdated or low-value details should decay over time.

The next generation of AI tools won’t be the ones that remember everything. They’ll be the ones that know what to forget. Developers building LLM applications should start by shaping working memory. Design for relevance at the contextual layer, even if persistent memory expands over time.

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Source: https://www.infoworld.com/article/3972932/why-llm-applications-need-better-memory-management.html