1. What’s your method for persistent memory in ChatGPT? (Prompt systems compared)

👤 u/Upstairs_Deer457  |  1 upvotes  |  2025-08-07  |  🏷️ General Discussion

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💡 Summary

I’ve been experimenting with ways to keep long-term or cross-session memory in ChatGPT and other LLMs, using only prompt engineering. There are two main approaches I’ve seen and used:

1. Command Prompt Method:

Super simple, works for most people who just want to save a fact or two:

/P-Mem_ADD [TEXT], [TAG]: Adds [TEXT] to persistent memory, labeled [TAG].
/Lt-Chat-Mem_ADD [TEXT], [TAG]: Adds [TEXT] to session memory, labeled [TAG].
/P-Mem_FORGET [TAG]: Overwrites persistent memory for [TAG].
/Lt-Chat-Mem_FORGET [TAG]: Removes [TAG] from session memory.
/P-Mem_LOAD [TAG]: Loads [TAG] into chat as a JSON object.

2. Framework Method (White Save Suite):

I ended up building something more structured for myself, since I wanted multi-slot context, summaries, and backup. Here’s a comparison:

	White Save Suite	Command Memory Manager
Power	⭐⭐⭐⭐⭐ (Framework + slots)	⭐⭐ (Quick facts)
Ease of Use	⭐⭐⭐ (Setup needed)	⭐⭐⭐⭐ (Instant-on)
Features	Slots, backups, audit, meta	Add/remove/load only
Scalability	High	Gets messy, fast
Data Integrity	Robust (summaries/backups)	Manual, error-prone
Customization	Extreme	Minimal

If anyone wants the full framework prompt or wants to compare setups, let me know in the comments and I’ll share. Really curious what the rest of this sub uses. I'm always down to swap ideas.

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2. How are you managing evolving and redundant context in dynamic LLM-based systems?

👤 u/steamed_specs  |  1 upvotes  |  2025-08-07  |  🏷️ Quick Question

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💡 Summary

I’m working on a system that extracts context from dynamic sources like news headlines, emails, and other textual inputs using LLMs. The goal is to maintain a contextual memory that evolves over time — but that’s proving more complex than expected.

Some of the challenges I’m facing:

• Redundancy: Over time, similar or duplicate context gets extracted, which bloats the system.
• Obsolescence: Some context becomes outdated (e.g., “X is the CEO” changes when leadership changes).
• Conflict resolution: New context can contradict or update older context — how to reconcile this automatically?
• Storage & retrieval: How to store context in a way that supports efficient lookups, updates, and versioning?
• Granularity: At what level should context be chunked — full sentences, facts, entities, etc.?
• Temporal context: Some facts only apply during certain time windows — how do you handle time-aware context updates?

Currently, I’m using LLMs (like GPT-4) to extract and summarize context chunks, and I’m considering using vector databases or knowledge graphs to manage it. But I haven’t landed on a robust architecture yet.

Curious if anyone here has built something similar. How are you managing:

• Updating historical context without manual intervention?
• Merging or pruning redundant or stale information?
• Scaling this over time and across sources?

Would love to hear how others are thinking about or solving this problem.

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📌 Final Takeaway

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