The Importance of Managing "Memory" in AI Development: Points Beginners Overlook

📑 Table of Contents

Introduction: The Misconception That AI "Learns"

Many beginners who start using AI tools like Genspark, ChatGPT, and Claude share a common misconception. It's the idea that "if you use the same chat screen for a long time, the AI will learn about you and become smarter and smarter."

However, in reality, this is a major misconception. AI does not "learn" through repeated conversations; in fact, it can even become "dumber" the longer you use it.

This article explains the mechanism of "AI memory" that AI beginners often overlook, and effective development strategies based on understanding it.

Human Memory vs. AI Memory: The Decisive Difference

Mechanism of Human Memory

As humans engage in conversations, they accumulate the other person's background, preferences, and way of thinking as long-term memories. The depth of understanding is completely different between a conversation with someone you just met and one with a friend you've known for 10 years, isn't it?

Mechanism of AI Memory

On the other hand, AI's "memory" has the following characteristics:

Item Human AI
Memory Storage Long-term storage in the brain Resets at session end
Learning Ability Learns and grows from experience Does not learn during conversation (fixed model)
Memory Capacity Virtually unlimited (may forget) Strict limits (token count)
Past Reference May take time to recall Completely forgets beyond limit
Important: AI only retains conversation history as "temporary working memory," which is completely erased after the session ends. It does not "learn and grow" like humans.

How AI Resets with Each Session

Limitations of the Context Window

The amount of information that AI "remembers" has a technical limit called the context window.

Context Window of Major AIs

  • GPT-4: Approximately 128,000 tokens (approx. 100,000 words)
  • Claude 3.5 Sonnet: Approximately 200,000 tokens (approx. 150,000 words)
  • Gemini Pro: Approximately 1,000,000 tokens (approx. 750,000 words)

*1 token ≈ 0.75 words (for English)

While it may seem "large capacity" at first glance, the following information quickly accumulates in actual development:

  • Conversation history (questions and answers)
  • Generated code
  • Error messages
  • Document content

As a result, in long conversations, older information is "forgotten" in order.

What Happens When You Start a New Chat?

When you open a new chat screen, the AI does not remember anything from the previous session. It's like a human suddenly getting amnesia.

Real-world Example

In Session 1:

"I am developing an e-commerce site using Node.js and Express. The database is PostgreSQL."

In Session 2:

"I want to implement the login function, continuing from yesterday." → AI remembers nothing of "yesterday's content"!

Beginner's Trap: The Illusion That "The Longer You Use It, The Smarter It Gets"

Misconception 1: AI Will Understand Your Project

Beginners often think that if they explain something repeatedly, the AI will understand, but in reality, an explanation is needed from scratch every time.

Misconception 2: A Long Conversation History is Advantageous

On the contrary. A long conversation history causes "memory fragmentation", leading to decreased AI performance.

Misconception 3: AI Learns from Past Mistakes

AI will repeat the same mistakes over and over again. This is because it does not "learn" during a conversation. Even if you correct an error, it may cause the same error in the next session.

Warning: The feeling that "AI understands you" is an illusion. Technically, it's the same as explaining to a stranger every time.

Basic Recording Strategy: The Importance of Documentation

Why is Documentation Necessary?

Since we cannot rely on AI's memory, information must be persistently stored by humans. This is documentation.

What Should Be Recorded?

  1. Project Overview
    • Purpose, target users, main features
  2. Technical Stack
    • Languages used, frameworks, libraries, versions
  3. Directory Structure
    • File arrangement, naming conventions
  4. API Information
    • Endpoints, authentication methods, environment variables
  5. Known Issues and Solutions
    • Bugs, errors encountered, and their remedies
  6. Development Decisions
    • Reasons "why this design was chosen"

Recording Format

Markdown files (.md) are recommended. The reasons are:

  • Easy for AI to read
  • Easy to version control
  • Platform-independent

Example of Basic Document Structure

📁 Project Root
├── README.md (Project Overview)
├── TECH_STACK.md (Technical Stack)
├── API_DOCS.md (API Specification)
├── CHANGELOG.md (Change Log)
└── QUICK_REFERENCE.md (Quick Reference)

How to Utilize AI Drive and File Saving

Genspark's AI Drive

Genspark has a feature called AI Drive, which allows you to save and manage files. By mastering how to use AI Drive efficiently, memory management becomes significantly easier.

Key Points for AI Drive Utilization

  1. Create documents in the conception phase
    • Save "Concept.md" at the start of the project
  2. Update regularly
    • Update whenever features are added or specifications change
  3. Load in new chats
    • Instruct: "Please read /aidrive/ProjectName/Concept.md"

Saving Methods with Other Tools

  • ChatGPT: Save separately to Google Drive or Notion
  • Claude: Register documents using the Projects feature
  • Local Development: Version control with Git

Practical Tips: How to Use "Recall" Prompts

Effective "Recall" Prompts

There's a technique to make AI "recall" important information during a new session or a long conversation.

Prompt Examples

Basic Form:

"To confirm again, this project is an application for [purpose] using [technical stack]. We are currently at the [current status] stage, and next, we will implement [what we want to do]."

Specific Example:

"To confirm again, this project is a blog system using Next.js + TypeScript. The article posting function has been implemented, and next, we will add the comment function."

Regular "Situation Summarization"

In long conversations, it is effective to insert prompts like the following every 10-15 interactions:

  • "Please summarize the progress so far."
  • "Please list what needs to be done next."
  • "Please organize the current issues."

Summary: Memory Management is Key to Successful AI Development

What AI development beginners most often overlook is the importance of "memory management." By grasping the following points, the efficiency of AI development will dramatically improve:

  • AI does not "learn": It does not get smarter through repeated conversations.
  • Sessions are "short-term memory": Completely reset after completion.
  • Documentation is essential: Humans must record information persistently.
  • Utilize AI Drive: Inherit information by saving files.
  • Regularly "remind" AI: Insert situation summaries in long conversations.
Conclusion: In AI development, instead of relying on "AI's memory," "human-side recording strategies" are the key to success. By mastering documentation and file management, AI can become a truly powerful partner.

As a next step, let's learn about the best timing for chat screen migration and how to use AI Drive efficiently to build an even more efficient development environment.

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