Tutorials Prompt Engineering Tutorial
AI Memory Systems — Complete Guide
AI Memory Systems — Complete Guide: free step-by-step lesson with examples, common mistakes, and interview tips — part of Prompt Engineering Tutorial on Toolliyo Academy.
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Prompt Engineering Tutorial · Lesson 54 of 100
AI Memory Systems
Prompts ✓ → Apps
Apps · 2 — RAG & agents · ~10 min · Module 6: AI Agents
What is this?
Agent memory stores facts across sessions: short-term (chat buffer), long-term (vector store of summaries), and working notes.
Why should you care?
PromptVerse Agent Memory writes tenant preferences after confirmed interactions.
See it live — copy this example
Copy the prompt into ChatGPT, Claude, or your LLM API playground and compare outputs.
after_turn:
summary = llm("Summarize durable facts from this turn: " + transcript)
if user_confirmed(summary):
memoryStore.upsert(user_id, summary)
next_turn:
inject memoryStore.get(user_id) into system contextWhat happened?
- Only confirmed summaries enter long-term memory.
- Injection personalizes without re-asking SSO provider each chat.
Practice next
- List what should NOT be memorized (PII).
- Summarize 3-turn chat to bullets.
- Confirm before save.
- Separate user vs org memory.
- Expire memories after 90 days.
Remember
Confirm before long-term store. Summarize to save tokens. TTL old memories.
Returning admin
Admin asks "use our usual tone again".
Outcome: Memory recalls formal tone preference from last month.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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