AI Self-Evolution (IV): From Experience to Skill

This is the final article in this series. The previous three covered how to talk to AI, how to develop with AI, and how to have multiple AI collaborate. This article covers something even more important: how to make AI better with every use.

Have you noticed that some people's efficiency with AI keeps increasing, while others keep stepping into the same pitfalls every time they use AI?

The difference isn't about who has better technical skills. It's about: the former summarizes, the latter doesn't.

Why Most People Don't Accumulate with AI

Most people's workflow with AI goes like this:

Encounter problem → Ask AI → Get answer → Problem solved → Done.

Next time a similar problem occurs, ask AI again → Get answer → Problem solved → Done.

Every time is a fresh start. Pitfalls from last time, you'll step on them again. Good approaches discovered last time, you'll have to rediscover them.

Why? Because experience only exists in your head and hasn't become something reusable. After finishing a project, your head is full of "next time I should do it this way" thoughts, but after a week you forget. Next time you do a similar project, you start from zero again.

Let AI Help You Summarize

The solution is simple: at the end of each project, have AI help you summarize.

Not having you write the summary yourself — that's too slow, and what you write often ends up as a running account. Let AI do it; you just provide the materials.

At the end of a project, send these things to AI:

• What this project did
• What pitfalls were encountered during the process
• How each pitfall was resolved
• Which practices worked well and can be reused
• Which practices didn't work and should be avoided next time

Then have AI organize this information into a structured document.

This document is your Skill — a reusable process template.

What Is a Skill

A Skill isn't a prompt template, isn't a tool recommendation list. It's an operations manual for a specific type of task.

For example, after a round of "using AI to develop a blog system," the Skill you summarize might look like:

Blog System Development Skill

Applicable scenarios: Using AI to develop a content management web project

Recommended breakdown: User system → Content system → Comment system → Admin backend

Known pitfalls:

• AI-generated user auth code defaults to JWT, but JWT has issues in server-side rendering scenarios; switch to session
• For infinite nested replies in the comment system, AI's first recursive query performs poorly; needs to be changed to a closure table
• When integrating a rich text editor, AI defaults to Quill, but Tiptap is more Markdown-friendly

Verification checkpoints:

• Login feature must test both "remember me" and "log out then log back in" scenarios
• Comment feature must test reply nesting beyond 5 levels

Recommended AI role setting: Full-stack developer, familiar with Node.js + React, code style leans toward concise and practical

See? This Skill includes breakdown strategies, known pitfalls, verification checkpoints, and AI role settings. Next time you do a similar project, you give this Skill to AI as context, and AI can directly skip the pitfalls you've already encountered.

This is the value of a Skill: it turns the tacit experience you accumulated in real combat into explicit knowledge that AI can understand and use.

How to Make Skills Better Over Time

Skills aren't one-time. Each time you do a similar project, you'll encounter new pitfalls and discover better approaches.

So Skills need continuous updating.

The workflow goes like this:

Project starts → Load the corresponding Skill as AI's context → Encounter new problems during development → Record new pitfalls and new approaches → Project ends → Update the Skill with new discoveries

Each cycle, the Skill becomes more complete and more accurate.

After a few iterations, your Skill will become a very powerful "domain expert." A new colleague who gets your Skill can skip months of exploration.

Building a Self-Evolution System

If you have higher ambitions, you can automate this summarization process.

At the end of each project, automatically trigger a "summary Agent." This Agent's task is: analyze the conversation records of this project, extract key experiences, and update the corresponding Skill document.

You can even have this Agent do more:

• Compare Skills across different projects to discover common patterns
• When two Skills conflict, prompt you to confirm which is more accurate
• Periodically organize the Skill library, marking outdated content
• When you start a new project, automatically recommend the most relevant Skills

This is the AI Self-Evolution System — it's not AI getting smarter on its own, but your way of using AI becoming more and more efficient.

The core idea is simple: break big problems into small ones, accumulate solutions to small problems into Skills, and let Skills make the next big problem easier to break down.

A positive cycle.

Summary

Four articles, a complete AI usage methodology:

• Part I: How to talk to AI — give identity, diverge thinking, plan before detailing, give framework not answers
• Part II: How to develop with AI — single feature loop, skeleton before flesh, verify before moving on
• Part III: How to have multiple AI collaborate — clear division, independent verification, environment isolation, break big into small
• Part IV: How to make AI better with every use — summarize experience, internalize as Skills, build a self-evolution system

But this methodology itself is also evolving. In actual use, you'll discover new pitfalls and new techniques. Following the method in Part IV, summarize these experiences into your own Skills.

AI tools are changing, but the underlying mindset of using AI won't change. Master this methodology, and no matter how AI upgrades, you'll be able to get up to speed quickly.

Previous: Multi-Agent Collaboration Patterns — Division, Isolation, and Verification

Extending Skills Across Your Organization

Skills become more powerful when shared across a team or organization. Create a shared Skill library in a shared Git repository, Notion database, or wiki. When someone encounters a new challenge and develops a good approach, document it as a Skill. Onboard new team members with Skills. Instead of walkthroughs that depend on someones availability, give new team members the Skill library as their first resource. Review and prune periodically. Schedule quarterly reviews to archive outdated skills, update evolved skills, and add skills for new challenges. Measure skill impact. Track whether using Skills actually improves outcomes — are projects completed faster, are there fewer bugs, are new team members productive sooner. The ultimate goal is an organization where knowledge compounds over time, where each project builds on the accumulated wisdom of previous projects.
Future Trajectory

Multi-agent systems let AIs critique and accelerate each other progress. Continual learning updates knowledge without catastrophic forgetting. Alignment research ensures AI systems pursue human-compatible goals.
Future Trajectory

Multi-agent systems let AIs critique and accelerate each other progress beyond any single model. Continual learning updates knowledge without catastrophic forgetting of previous skills. Federated learning enables training on decentralized data without sharing raw data. Self-supervised pre-training produces models needing less fine-tuning. The critical question is how humans will steer AI self-evolution toward beneficial outcomes. Alignment research ensures increasingly capable AI systems pursue goals compatible with human values.

Applications

Design feedback loops capturing user interactions as training signals for continuous model refinement. Monitor both improvements and potential regressions in system behavior consistently. Start with narrow measurable tasks.

The Feedback Loop of AI Self-Improvement

The mechanism works through a continuous cycle: 1) Experience - AI processes a task and observes the outcome, 2) Comparison - Compare actual result against expected result, 3) Analysis - Identify what went wrong or what could be improved, 4) Adaptation - Update internal patterns or strategies, 5) Verification - Test the adaptation on similar tasks.

Real-World Applications

Code review tools that learn from developer feedback to catch new categories of bugs. Recommendation systems that adapt to changing user preferences over time. Autonomous driving systems that improve edge case handling through accumulated experience. Customer service bots that learn from successful resolution patterns.

Limitations and Risks

Current AI self-evolution has important constraints: Changes are incremental not revolutionary, risk of overfitting to recent experiences, difficulty in transferring learned skills across domains, and potential for unintended behavioral drift. Human oversight remains essential to ensure AI self-improvement aligns with intended goals and ethical boundaries.