AI System Optimization
How to set AI up as a reliable partner: structured context, tools you own, automated routines.
By James Schramko · Updated May 2026.
Most people treat AI like a search engine. They ask random questions and get random results. The output is inconsistent because there is no system behind the input.
The fix is structural. You give AI context it can rely on, you work inside tools you own, and you let it run the repeatable work for you. This playbook covers how to set that up.
Claude First
Claude is the primary tool. Most of the real work happens in Claude Code, which reads and writes your files directly, so the AI works on your actual business material instead of a copy pasted into a chat window.
ChatGPT has one job in this setup. It is a second opinion. Paste in something you have already written or decided and ask whether it is clear, relevant, and good. Use it to cross-check, not to produce.
One primary tool used well beats five tools used shallowly.
Your Context Lives in a Vault
Do not rely on scattered chat memory. A chat remembers fragments from dozens of conversations and cannot connect them into anything coherent.
Keep your business context in one place you control. A structured set of files holds your business model, your voice, your frameworks, your client context, and your decisions. An Obsidian vault works well for this.
Claude Code reads that vault at the start of a session. Context is consistent every time, instead of re-explained every time.
This is also an ownership decision. Your context is an asset and it should live in files you own, not inside a vendor's database. The Sovereign AI Setup playbook covers the full reasoning.
Two Projects, Not Many
The common advice is to split AI into many specialised projects, one for each function. In practice that creates overhead, duplication, and drift. You spend more time managing projects than working.
Keep it to two chat projects.
- One project for brainstorming and thinking out loud.
- One project as an archive for finished threads worth keeping.
Delivery work does not happen in chat projects. It happens in Claude Code against the vault. The chat projects are for ideas.
Prompt It Properly
Output quality depends on how you structure the request. A vague prompt produces vague work.
Use a consistent prompt structure for anything that matters. The AI Prompting Formula playbook covers the structure to use.
Automate the Repeatable Work
Anything you do on a regular cycle, such as a weekly review, a digest, or a scan, should not be done by hand each time.
Use routines: scheduled AI agents that run on their own and deliver the result where you want it. Routines replace manual reminders and calendar schedules. You set them once and they run.
The AI Task Automation playbook covers how to build them.
Calibrate Before You Rely On It
Once the setup is in place, test it with real scenarios before trusting it for decisions.
- Strategy test: ask it to advise on a real client situation using your frameworks.
- Voice test: ask it to write something short in your communication style.
- Sales test: give it a real objection and ask how to respond.
Good signs: it uses your frameworks, it sounds like you, and the advice is specific to your situation. Bad signs: generic business advice, clichés, and a voice that could belong to anyone.
Maintain It
The setup degrades if you ignore it. Stale context produces stale output.
- Keep the vault current. When something changes in the business, update the files.
- Remove context that is no longer true. Contradictions confuse the output.
- Re-run the calibration tests if quality drops.
Common Mistakes
- Treating AI as a search engine instead of building a system around it.
- Spreading work across many tools and many projects.
- Letting context live only in chat memory, where you cannot see it or control it.
- Setting it up once and never maintaining it.
The difference between average AI use and reliable AI use comes down to the system you build around it, not the model you use.