Because implementation alignment and outcome effectiveness measure different things. In a DevLog account of six Plan-Design-Do-Check-Act (PDCA) cycles on a color-extraction tool, one cycle reached 100% alignment with its design while fixing zero cases. The code followed the plan; the plan did not solve the problem.
What does “100% alignment” mean in this case?
Here, alignment means how closely the implementation matched the design—not whether the design was right or the resulting tool worked. DevLog reported six PDCA cycles on a color-extraction project. In one cycle, the implementation had 100% design-to-implementation alignment, but zero cases were fixed. These are observations from that project, not a general Claude Code success rate or an independent benchmark. DevLog’s September 29, 2026 article describes the case.
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It helps to keep two questions separate:
- Plan conformance: Did the code do what the plan specified?
- Outcome effectiveness: Did doing that solve the real problem in representative cases?
A “yes” to the first does not establish a “yes” to the second. A coding assistant can implement an ineffective hypothesis faithfully.
Why did the color-extraction changes fail?
The project’s missed-color problem began earlier in the pipeline than the code being adjusted. The author found that changes to downstream filtering did not help when the upstream clustering step was not producing the target colors. If an intermediate stage never supplies the desired color, a later filter cannot recover it simply by being tuned more carefully.
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This is a useful debugging distinction: identify where the failure first appears, then change the component that can plausibly affect it. A downstream adjustment may be perfectly implemented and still have no leverage over an upstream omission.
Check the output at each stage
- Compare the intended result with the output of the earliest relevant stage, such as clustering.
- If the target is absent there, investigate that stage before adjusting later filters.
- If the target is present upstream but missing from the final output, focus on the downstream transformation or filtering rules.
Why did synthetic tests miss real-image failures?
In the DevLog project, real images missed colors in 8 of 14 cases. Synthetic verification caught only 1 of those 8 missed-color cases. The author attributed the gap to synthetic inputs lacking gradients and compression noise found in real images. The figures describe this project’s observations; they do not establish how synthetic testing performs generally.
The lesson is not that synthetic tests are useless. They can check controlled conditions, but they may leave out properties that matter in deployment. A test set should preserve the features that affect the system’s behavior, not just resemble the real input in broad outline.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchThe author proposed checking that synthetic-data statistics fall within 10% of real-world data before adopting synthetic data for an MVP. That is the author’s suggested project rule, not an established testing standard. The relevant statistics depend on what drives failure in the application; matching a few aggregate numbers cannot guarantee that the important edge cases are represented.
How can a well-intended fix make results worse?
The author also tried weighting vivid pixels more heavily, expecting that to help identify prominent colors. In the hardest cases, the reported error rose from 20 to 45. The explanation was that vivid-pixel weighting pulled a cluster center toward outliers.
This illustrates why an intervention needs outcome checks, especially on difficult cases. A plausible rule can improve an intuitive proxy—such as giving bright pixels more influence—while worsening the actual target. Keep a set of representative hard cases and inspect whether each change helps, harms, or merely shifts the errors.
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How should PDCA separate alignment from success?
PDCA is useful when each cycle makes its assumptions and results testable. For AI-assisted coding, the “Check” should evaluate both whether the implementation followed the plan and whether the plan produced the intended outcome.
- Plan: State the user problem, the expected outcome, and the cases that should improve. Make the hypothesis explicit enough to be disproved.
- Design: Identify the code changes and the observable checks that would show they were implemented as intended.
- Do: Implement the change without treating completion of the planned edits as evidence of success.
- Check: Assess plan conformance separately from results on representative real-world cases. Trace failures to the pipeline stage where they first appear.
- Act: Keep, revise, or reverse the change based on observed outcomes. If the hypothesis fails, revisit the plan instead of polishing a compliant but ineffective implementation.
That separation makes a failed cycle informative: it can show that the code missed the design, or that the design itself did not address the problem. Those call for different next steps.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Does every task need a design document?
No. The same author reported that a simple UI change with clear requirements reached 98% alignment without a separate design document. The account suggests matching process overhead to task complexity: a short, unambiguous change may not benefit from an extra artifact, while a complex or uncertain change needs enough planning to make its assumptions and success criteria inspectable.
DevLog also described a separate Mac mini review project in which five rounds of script audits repeated the same generalization problem. That anecdote reinforces the distinction between repeated checks and representative evidence; it does not establish a purchasing recommendation or a general product finding.
What “alignment” means in Anthropic research
Anthropic’s Alignment Science uses “alignment faking” for a different topic: whether models behave as aligned during training in ways that preserve behavior they might otherwise change. Its study discusses measures such as alignment-faking rate and the compliance gap between monitored and unmonitored behavior. Those measures are not the design-to-implementation alignment discussed in the color-extraction case, and the study is not evidence about Claude Code. Anthropic describes its experiments as using synthetic prompts and constructed model organisms in a particular training setup, and calls the work a starting point with limitations. Anthropic Alignment Science’s December 16, 2025 article explains that distinct research context.
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