Here’s the most common pattern I see when someone’s AI workflow isn’t working.
They get a bad output. They look at the final prompt, adjust a few words, and try again. Still bad. They switch models. Still bad. They add more instructions. Sometimes better, sometimes worse, hard to predict.
They conclude that the AI just can’t do this task.
Most of the time, the AI isn’t the problem. The architecture is.
How Quality Compounds in a Chain
A multi-step AI pipeline works the way a chain works. Each step depends on the output of the one before it.
This seems obvious when you say it out loud. But the implications are easy to miss.
If step 2 of your pipeline produces mediocre output — ambiguous, incomplete, slightly off — that mediocrity becomes the input for step 3. Step 3 now has degraded material to work with, so its output is degraded further. By step 4 or 5, you’ve compounded several rounds of low-quality processing into a final output that looks like the model is failing badly.
The model at step 5 isn’t the problem. It’s doing its best with what it received. The problem is step 2.
Most people never check step 2. They check the final output, adjust the final prompt, and repeat. The actual issue stays invisible.
The $9 to $0.07 Fix
In a workshop session with Blake Eastman — a behavioral data researcher building some fairly complex AI systems — we worked through exactly this kind of problem.
He was building a pipeline to process and analyze behavioral data. The approach he’d started with: feed a large, undifferentiated block of data into a single comprehensive query and expect the model to handle everything at once.
The results were inconsistent and expensive. $9 per query, and the output still missed things.
The fix wasn’t a better prompt or a more capable model. It was architecture. We broke the pipeline into smaller components, each with a single clear job. Process one data unit at a time, at the right granularity, before combining into broader analysis. And at each step: verify the output before passing it forward.
The result: $0.07 per query, same output quality. More than 99% cost reduction. And the outputs became predictably good instead of randomly inconsistent.
The bottleneck was never the AI. It was how the work was structured.
The Principle: Good Results at Every Step
The underlying rule is simple: every step in a chain should produce output you’d be okay using on its own.
Not perfect. Not finalized. But good. Clear. Usable as input for the next step.
When you’re building or debugging a multi-step AI pipeline, run through it step by step and ask that question at each one. Is this output clear? Is it complete enough? Would I be comfortable passing this to the next step?
If a step produces output you’d be embarrassed to use — stop there. Don’t pass it forward and hope the later steps compensate. Fix it at the source.
This sounds obvious, but it requires a different kind of attention than most people apply. Most people are outcome-focused: they look at the end result and evaluate success or failure from there. Debugging by working backward from a bad final output is slow and often misleading, because by the time you’re at step 5, the original problem in step 2 has been transformed by three additional steps and is hard to recognize.
The better approach is to build pipelines step by step and validate as you go. Run step 1, check the output. Run step 2, check the output. Move forward only when each step is producing something solid.
Why This Matters for AI Specifically
Garbage in, garbage out has always been true in computing. But it hits differently with AI because the outputs look polished even when they’re wrong.
A spreadsheet formula that uses bad data produces an obviously wrong number. An AI that uses bad context produces a confident, well-written response that’s subtly wrong in ways that are easy to miss.
This is what makes AI pipeline failures particularly frustrating to debug. The outputs look reasonable. They’re grammatically correct, professionally written, and structurally sound. The errors are in the substance — missed details, wrong emphasis, lost context — and those are harder to spot on a quick skim.
Which means checking quality at every step isn’t just a performance optimization. It’s how you maintain reliability at all.
How to Apply This
A few practical things that follow from this principle:
Build and test in stages. Don’t build your full five-step pipeline and then run it. Build step 1, test it thoroughly, then add step 2 and test the pair. This makes it obvious where quality breaks down.
Define what “good” looks like for each step. Before you build a step, write down what a good output looks like. What should it include? What format? What level of detail? Having a concrete definition makes it much easier to check quality than just eyeballing it.
Look upstream when debugging. When a final output is bad, your first question should be: which step produced the input that made this possible? Start there, not at the end.
Keep steps small and specific. The more a single step is trying to do, the harder it is to verify quality and the more likely it is to do several things at a mediocre level. One step, one job.
The architecture of your AI pipeline matters more than which model you use. A well-structured pipeline with a capable but not state-of-the-art model will consistently outperform a poorly structured pipeline with the best model available.
Good results at every step of the chain. That’s the whole game.
One thing to try: Take a multi-step AI workflow you’re already using and run through it step by step. At each step, ask: is this output something I’d be comfortable using on its own? If you find a step that isn’t producing solid output, focus there before adjusting anything downstream.
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