How to Get Better AI Output: Build a Context System That Actually Works

"If it's only in your head, it does not exist."

I say this in every talk, every workshop, every client call. Because it's the single biggest reason people get bad results from AI.

You know your clients. You know what worked last quarter. You know the quirks, the preferences, the things that failed. But Claude cannot read your mind. And 85% of that knowledge lives undocumented — scattered between your head, your email, and random Notion pages.

No system to capture it means AI has nothing to work with.

Remember the jar

Every conversation with an AI starts from zero. Think of the context window as a jar. Limited space.

Everything goes into that jar: your instructions, your questions, the AI's answers, the data it reads. When the jar fills up, the AI starts summarizing older stuff to make room. It slowly forgets what you talked about earlier.

This is why structure matters. What goes in the jar — and in what order — determines the quality of everything that comes out.

The jar is not even

Here's something most people don't know: the jar doesn't treat all information equally.

Stanford research called "Lost in the Middle" showed that LLMs pay the strongest attention to information at the beginning and end of their context window. The middle? That's the weak spot.

The effective context is often only 50-70% of what's advertised.

This is why what goes in first matters most. Your core instructions, your business context, your rules — put those at the top. They get the strongest attention.

The context hierarchy

Think of it as layers, loaded in order of importance:

1. The map — Your instructions file. Loaded automatically every session. It tells the AI what this project is, where to find things, and what rules to follow.
2. The business brain — Goals, KPIs, targets, hard constraints. This one file shapes every recommendation the AI makes.
3. Supporting context — Brand voice, performance data, work history, competitor intel. Loaded as needed for specific tasks.

That's the whole system. A map, a brain, and supporting files.

Thin context vs rich context

Here's what happens without context:

You: Write me some ad headlines.

AI: "Buy Running Shoes Online" / "Free Shipping Available" / "Shop Now & Save"

Generic. Could be any brand. Useless.

Now here's what happens when the AI has your business context, brand voice, targets, and historical data:

You: Create headlines.

AI: Based on the brand voice, CPA target of $50, and Q1 push — here are 45 variations ready for import. "Run Faster in Nike Air Max" / "Free Returns — Try Before You Buy" / "Join 12M+ Runners."

Same AI. Same capability. Completely different results. The only difference is what was in the jar.

Everything is a markdown file

Your context library doesn't need to be complicated. Most of mine are simple text files:

• Business context — goals, KPIs, constraints, priorities
• Brand voice — how the client sounds, what they never say
• Work history — what you did last session, open items, decisions made
• Meeting notes — even rough bullet points work
• Client emails — copy/paste the key ones about strategy

Simple text. Markdown files. That's it. The AI reads it all.

Even messy notes are better than no notes.

The compound effect

This is where it gets interesting. Context compounds.

Week 1, you fill in the basics — goals, KPIs, constraints. The AI gives decent answers.

Week 4, you've got memory logs growing, competitor intel added, a few client emails in there. The AI gives good answers.

Week 8, you have a full context library. The AI gives answers that sound like they came from someone who knows the account as well as you do.

Each session adds to your context library. Every note you add today pays dividends in every future session.

Common mistakes

Five things I see people get wrong:

• Empty business context — No targets means generic advice. Fill it in.
• Outdated info — Last quarter's targets lead to wrong optimizations. Keep it current.
• No context, just commands — Running tools without brand context produces bland results.
• Never saving work history — The AI starts fresh every time. No continuity. No memory.
• Dumping everything unstructured — 50 random files with no map to guide reading. That's not context, that's noise.

Clean structure = less wasted tokens = better answers.

The most important AI skill

The best AI skill isn't prompting. It's note-taking.

Start taking notes. Write down what you know. Structure it. Keep it updated. Hand it to AI.

The models are already smart enough. Your input is the bottleneck.

Start there.