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When the AI forgets previous interactions, provides inconsistent information, or fails to remember important details, you’re experiencing memory or context issues. Learn how to identify and resolve these problems.

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Common memory problems

Symptom: AI doesn’t remember details from earlier in the same conversationCauses:
  • Context window limit exceeded
  • Very long conversation history
  • Model limitations
  • Information not properly retained
Solutions:
  1. Summarize key points: Explicitly remind the AI of important details
  2. Use Memory feature: Enable Memory to retain facts across chats
  3. Start new conversation: Archive old chat and start fresh with summary
  4. Reference specific messages: Point to earlier responses
  5. Reduce context: Remove unnecessary messages
Effective reminder
"As we discussed earlier about the user authentication system with JWT tokens..."
Most models have context windows between 8K-200K tokens. Long conversations consume this limit, causing older messages to be truncated.
Symptom: AI provides different answers to similar questions in different conversationsCauses:
  • Memory not enabled or not working
  • Different models used
  • Information not saved to Memory
  • Conflicting custom instructions
Solutions:
  1. Enable Memory: Turn on Memory feature
  2. Verify memory content: Check what’s stored in Memory settings
  3. Use same model: Stick with one model for consistency
  4. Set custom instructions: Create consistent instructions
  5. Share context explicitly: Don’t rely solely on Memory
Memory is model-specific in some cases. Information stored while using Claude may not transfer to GPT-4 sessions.
Symptom: AI references old, incorrect, or obsolete informationCauses:
  • Memory not updated with new information
  • Conflicting facts stored
  • Manual memory editing needed
Solutions:
  1. Navigate to Memory settings
  2. Review stored information
  3. Delete outdated entries
  4. Add corrected information
  5. Confirm changes are applied
See managing Memory for detailed instructions.
Symptom: AI forgets critical facts you’ve shared multiple timesCauses:
  • Information not structured for retention
  • Memory feature disabled
  • Details not deemed significant by AI
  • Storage limits reached
Solutions:
  1. Explicitly state importance: “Remember this for future reference…”
  2. Use structured format: Present information as facts or lists
  3. Verify memory storage: Check Memory settings to confirm storage
  4. Repeat in multiple conversations: Reinforce important details
  5. Use custom instructions: Add to custom instructions instead

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Understanding context windows

AI models process conversations within a context window - a limited amount of text they can reference at once.What happens when limit is reached:
  1. Oldest messages are dropped
  2. AI can’t reference removed content
  3. May appear to “forget” earlier discussion
Typical context windows:
  • GPT-4 Turbo: 128K tokens (~96,000 words)
  • Claude 3.5 Sonnet: 200K tokens (~150,000 words)
  • Gemini 1.5 Pro: 2M tokens (~1.5M words)
  • GPT-4o: 128K tokens (~96,000 words)
One token ≈ 0.75 words on average. Conversation includes both your messages and AI responses.

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Fixing memory issues

1

Enable and verify Memory

  1. Navigate to Settings → Memory
  2. Enable the Memory feature
  3. Confirm it’s active (indicator should show enabled)
  4. Test by asking “What do you remember about me?”
Memory is working when AI can recall facts from previous conversations.
2

Review stored memories

  1. Open Memory management page
  2. Read through stored information
  3. Verify accuracy of remembered facts
  4. Look for conflicts or outdated data
Memory management interface
3

Clean up memory

Remove or update incorrect information:
  1. Click edit on any memory entry
  2. Correct the information or delete entirely
  3. Add new facts if needed
  4. Export backup before major changes
See memory management guide for details.
Resetting memory deletes all stored information permanently. Export first if you want to preserve anything.
4

Optimize conversation structure

For complex projects, structure conversations effectively:Project chat organization:
Main project chat: High-level decisions and planning
├── Implementation chat 1: Feature A development  
├── Implementation chat 2: Feature B development
└── Bug fixes chat: Issues and resolutions
Use conversation branches to explore alternatives without cluttering main discussion.

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Memory feature best practices

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What to store in Memory

  • Personal preferences and work style
  • Project requirements and specifications
  • Technical environment details (languages, frameworks)
  • Business context and domain knowledge
  • Recurring tasks and patterns
  • Team structure and responsibilities
  • Important deadlines and milestones
Example:
"I work primarily in React and TypeScript"
"My projects use PostgreSQL database"
"I prefer functional programming patterns"
"My team follows Agile methodology"
  • Sensitive personal information
  • API keys, passwords, or secrets
  • Constantly changing data
  • Temporary project details
  • Conversation-specific context
  • Very detailed technical specifications
Never store passwords, API keys, tokens, or other sensitive credentials in Memory. Use secure credential management instead.

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Structuring information for retention

Effective memory statements:
  • Clear and concise
  • Factual, not conversational
  • Specific and actionable
  • Updated when circumstances change
We talked about how I might want to maybe use Python sometimes for data stuff when I'm working on projects that involve data analysis or something similar to that.

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Advanced troubleshooting

Possible causes:
  • Browser cookies disabled
  • Using incognito/private mode
  • Different device/browser
  • Account sync issues
Solutions:
  1. Enable cookies in browser settings
  2. Use regular browsing mode
  3. Check account sync status
  4. Try logging out and back in
  5. Contact support if issue persists
When Memory contains contradictory information:
  1. Identify conflicts: Review all stored memories
  2. Prioritize current info: Delete outdated facts
  3. Be explicit in prompts: Override Memory when needed
  4. Reset if necessary: Start fresh if too complex
Overriding Memory in prompts:
Despite what you may remember about my language preference, 
for this project I'm using Go instead of Python.
Memory is stored securely and never shared:
  • Encrypted at rest and in transit
  • Only accessible to your account
  • Not used for model training
  • Can be exported or deleted anytime
See data and compliance for details.
You can export or delete all Memory data from your account settings at any time.

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Related pages

How Memory works

Understand Memory feature in depth

Manage Memory

Edit, export, and reset Memory

Token limits

Understanding context windows

Custom instructions

Set persistent conversation guidelines