How to Build a Reasoning Architecture: Step by Step

If you have read What Is a Reasoning Architecture, you understand the concept. This article is the practical guide. By the end, you will have a working reasoning architecture you can deploy to Claude, ChatGPT, or any AI tool that accepts system-level context.

The process takes 30 to 45 minutes. It requires honesty, not technical skill.

Before you start

You will need:

• 45 minutes of uninterrupted time
• Access to your recent work (emails, documents, decisions from the past month)
• A blank document or your Learned Context workspace

The goal is not perfection. The goal is a first version that is specific enough to change your AI's output. You will refine it over the first few weeks as you notice gaps.

Step 1: Decision audit (15 minutes)

Pull up 10 significant decisions you have made in the past 3 months. These do not need to be major strategic choices. Hiring decisions, project prioritisations, resource allocations, and client recommendations all count.

For each decision, ask yourself:

• What was the first thing I checked?
• What information did I weight most heavily?
• What did I explicitly choose to deprioritise?
• What would have changed my mind?

Look for patterns. You will find that you have consistent first moves, consistent weights, and consistent things you ignore. These are your prioritisation principles.

Write 3 to 5 prioritisation principles. Be specific. "I prioritise client value" is too vague. "Revenue-generating work comes before operational improvements, unless the operational issue affects client delivery timelines" is useful.

Step 2: Capture anti-patterns (10 minutes)

Anti-patterns are the mistakes you have learned to watch for. They often start with "I used to..." or "The common mistake is..." or "What most people miss is..."

Sources of anti-patterns:

• Mistakes you made early in your career and corrected
• Errors you have seen peers make repeatedly
• Assumptions that your domain consistently violates
• Patterns that look right but produce bad outcomes

Write 5 to 7 anti-patterns. Frame each as a specific warning, not a general principle.

Examples:

• "Do not confuse a confident stakeholder with a well-informed one. Ask for evidence."
• "If the first analysis confirms the hypothesis, actively look for disconfirming evidence before presenting."
• "Solutions that require coordination across more than three teams almost always take twice as long as estimated."

Step 3: Define quality standards (10 minutes)

For each type of work you produce regularly, write one to two sentences defining what "good enough" means. Quality standards vary by audience and stakes.

Step 4: Document risk tolerance (5 minutes)

Where do you draw the line? Risk tolerance is personal and contextual. Most professionals have never made it explicit.

Write 3 to 5 risk tolerance statements:

• What risks do you accept as normal?
• What risks do you always mitigate?
• What risks are never acceptable?

Example: "I will accept directional accuracy on internal market sizing. I require source verification on any number that goes to a client. I never share financial projections without sensitivity analysis."

Step 5: Note verification habits (5 minutes)

What do you always check before signing off? This is your mental checklist, the one you run unconsciously before hitting send.

Examples:

• "Verify numbers against the original source, never against a summary or intermediate document"
• "Before sending a recommendation, identify the strongest objection and address it"
• "Check every commitment against the actual calendar, not the stated timeline"

Putting it together

Your reasoning architecture document should be 400 to 800 words. Longer is not better. The goal is density: every sentence should change how your AI responds.

Structure it as:

## Prioritisation Principles
[3-5 specific principles]

## Anti-Patterns
[5-7 specific warnings]

## Quality Standards
[Per work type, 1-2 sentences each]

## Risk Tolerance
[3-5 tolerance statements]

## Verification Habits
[3-5 checklist items]

Deploying it

Once your reasoning architecture is written, deploy it:

Claude Projects: Add as Project Knowledge. It loads at every conversation start.

ChatGPT: Paste into Custom Instructions or upload as a file to a GPT.

Any MCP-compatible tool: Connect via Learned Context and your reasoning architecture loads automatically, alongside your professional context and operational data.

Membership includes the inference engine, conductor, and deployment guides for carrying your reasoning architecture across platforms as you refine it.

What to expect

The first time you use your reasoning architecture, the difference is obvious. Your AI stops producing generic framework outputs and starts producing work that reflects your professional judgement.

Over the first week, you will notice gaps. Situations where the AI makes a choice you would not have made. Add the missing principle or anti-pattern. Your reasoning architecture compounds: every refinement improves every future conversation.

This is what separates context engineering from prompt engineering. Prompts are disposable. Your reasoning architecture is permanent, portable, and gets better over time.

Start by assessing where your context gaps are, then build your reasoning architecture using this guide or inside Membership.