AI Coding Tip 009 - Compact Your Context

#webdev #programming #tutorial #ai

Stop the memory rot

TL;DR: You can keep your AI sharp by forcing it to summarize and prune what it remembers (a.k.a. compacting).

Common Mistake ❌

You keep a single, long conversation open for hours.

You feed the AI with every error log and every iteration of your code.

Eventually, the AI starts to ignore your early instructions or hallucinate # AI Coding Tip 009 - Compact Your Context

Stop the memory rot

TL;DR: You can keep your AI sharp by forcing it to summarize and prune what it remembers (a.k.a. compacting).

Common Mistake ❌

You keep a single, long conversation open for hours.

You feed the AI with every error log and every iteration of your code.

Eventually, the AI starts to ignore your early instructions or hallucinate nonexistent functions.

Problems Addressed 😔

Context Decay: The AI loses track of your original goals in the middle of a long chat.
Hallucinations: The model fills memory gaps with hallucinations or outdated logic.
Token Waste: You pay for the AI to re-read useless error logs from three hours ago.
Reduced Reasoning: A bloated context makes the AI less smart and more prone to simple mistakes.

How to Do It 🛠️

Restart often: You can start a new chat once you finish a sub-task.
Request a State Summary: Before you close a conversation, ask the AI to summarize the current decisions and plan.
Add Human Checkpoints: After the summary, confirm you are still on track.
Use Markdown Docs: Keep a small context.md file with your current stack and rules.
Prune the Logs: You should only paste the relevant 5 lines of a stack trace instead of the whole irrelevant 200-line output.
Divide and conquer: Break large tasks into smaller ones, invoking their own skills with local tokens and a fresh context.
Divide the responsibility: A General doesn't need to know what every soldier is doing on the battlefield.
Create and persist as Skill: After you have taught the AI, you should refactor the knowledge and business rules.
Keep an Eye on the Context Size: Most tools have visual indicators of the window consumption.
Use Local Persistence: Some tools allow sharing memory among agents and their sub-agents.

Benefits 🎯

You get more accurate code suggestions.
You avoid divergences
You follow the AI's train of thought.
You spend less time correcting the AI's hallucinations.
The AI follows your project constraints more strictly and keeps focused on your tasks

Context 🧠

Large Language Models have limited attention.

Long context windows are a trap.

Many modern models offer a very large context window.

In practice, they ignore a lot of them to your frustration.

Even with huge context windows, they prioritize and focus ob the beginning and the end of the prompt.

Prompt Reference 📝

Bad Prompt

Here is the 500-line log of my failed build. 

Also, remember that we changed the database schema 

Three hours ago in this chat.

Add the unit tests as I described above.

Now, refactor the whole component.

Good Prompt

I am starting a new session. Here is the current state: 

We use *PostgreSQL* with the 'Users' table schema [ID, Email]. 

The AuthService`interface is [login(), logout()]. 

Refactor the LoginComponent` to use these.