Not a ChatGPT wrapper
This is a guide to the architecture decisions that separate demo projects from systems that run every day. The difference isn't the model. It's the engineering around it.
Step 1: Start with the workflow, not the model
Before you pick GPT-4 vs Claude vs Llama — map the actual process.
Who does what? Where are the decisions? What data moves between steps? What are the inputs and outputs at each stage?
The workflow dictates the architecture. The model is one component in a larger system. If you start with the model, you'll build a solution looking for a problem. If you start with the workflow, you'll build a system that fits.
Draw the workflow on a whiteboard. Every box is a step. Every arrow is a data handoff. Every diamond is a decision point. This diagram is your architecture blueprint.
Step 2: Define the decision boundaries
Not every step needs AI. This is the single most important design decision in any AI system.
Most workflows are 80% deterministic logic, 20% judgment. The deterministic parts — routing, formatting, validation, notification — should be code. Plain, testable, predictable code.
Use AI only where judgment is required: classification, summarization, draft generation, pattern recognition. Each AI step should have a clear input, a clear output, and a clear fallback when the model underperforms.
Fewer model calls = faster, cheaper, more reliable. Every model call you remove is a point of failure eliminated.
Step 3: Design the failure modes
Every AI system will be wrong sometimes. The question isn't whether — it's what happens when it is.
Build review gates at critical decision points. Route low-confidence outputs to a human. Log every decision with reasoning so you can audit later.
A system without failure handling is a system waiting to break in production. The failure modes you design today are the trust you build over the next year.
Specific patterns that work:
- Confidence thresholds. Below 0.7 confidence? Route to human review.
- Structured fallbacks. If the model can't classify, default to a safe category and flag for review.
- Decision logging. Every AI output records: input, output, confidence, reasoning, timestamp. This log is your debugging tool, your audit trail, and your training data.
Step 4: Build the feedback loop
The system gets better when it processes real data from real users. But only if you capture the signal.
Track which outputs get edited by humans. Capture corrections. Measure what the human changes and why. Record the delta between the AI's suggestion and the final output.
This feedback data is more valuable than any fine-tuning dataset you'll find online. It's your system's data, from your domain, reflecting your team's judgment.
Use it to:
- Tune confidence thresholds
- Identify systematic blind spots
- Prioritize which AI steps need improvement
- Build evaluation sets for regression testing
Step 5: Ship it. Then iterate.
Don't wait for perfect. Ship the 80% version to 3 users.
Watch what they actually do. Watch where they override the system. Watch where they trust it. Watch where they don't even notice the AI is running — that's the best signal of all.
That observation is your roadmap. Not a product manager's assumptions. Not a stakeholder's wish list. Real usage data from real operators.
The first version of every production AI system is wrong in ways you can't predict from a whiteboard. Ship it, measure it, fix it. The iteration cycle is the product.
This is how we build
Every system at DK1.AI follows this discipline. Workflow first. Decision boundaries. Failure modes. Feedback loops. Ship fast. Iterate on real data.
If you have a workflow that should be automated — tell us about it.