I once asked an AI to build a brand website. What came back looked convincing at first glance, but it was broken in the ways that actually matter. The header showed exactly two links. There was no "About" page anywhere. An admin panel existed on the backend, yet no button or route on the frontend could actually reach it. The server side functioned. The user side did not.
Most people who run into this assume the AI got lazy or hit a token limit. That is not what is happening. The issue is structural. AI coding tools are built to police internal consistency. They ask: "Does everything I declared line up with itself?" They do not ask: "Does this kind of deliverable contain everything it is supposed to contain?" If you mention two pages for your brand site, the model dutifully checks whether those two pages link back to each other. Once the links resolve, it considers the task complete. It has no inherent notion that a brand site needs an About page, trust signals, or a contact path. There is no standard in its head.
I call the fix a Completeness Baseline.
A completeness baseline is simply a standard checklist of what a given deliverable must contain. You generate the artifact, then you check the real output against this external standard instead of trusting the tool's internal sense of "done."
Checking Across Three Layers
Not all missing pieces are equally obvious. A useful baseline checks three distinct layers.
Existence. Does the part actually exist? This sounds basic, but an AI will happily build a navigation wrapper that references a dashboard page without ever generating the dashboard page itself. The reference exists. The target does not.
Reachability. Can a real user actually get to it? Hidden admin panels are the classic symptom. The route and the component might both exist in the codebase, yet no menu item, button, or redirect exposes them to the interface. If a user cannot trip over it during normal use, it is not really there.
Substantiation. Is there real data or structure behind the surface? A page that loads but contains only placeholder text and no image upload slot is a skeleton wearing a costume. An About page without an image slot or editable text field is not complete, even if the HTML renders cleanly.
These three layers catch different classes of gaps. Existence finds the missing shoe. Reachability finds the shoe locked in a closet. Substantiation finds the shoe with no sole.
Baselines by Type
One baseline will not cover every project. You need to classify what you are building and define the non-negotiables for that category.
Brand sites need specific sections and reachable pages. Think About, Contact, privacy links, and navigation that actually exposes every major view.
APIs need documentation, exhaustive error codes, and rate limits. A working endpoint that returns 200 OK for success and a generic 500 for everything else is not a finished API. It is a hazard.
Automations need logs and failure alerts. If a workflow breaks at 2 AM and nobody knows until a human checks the run history, the automation is incomplete. Observability is not a bonus feature. It is part of the deliverable.
Once you name the category, the baseline writes itself. The hard part is enforcing it after the code is generated.
Design Rules That Hold Up
I rebuilt my own workflow around this idea and landed on three practical rules that keep projects from slowly unraveling.
Capability-gated design. Before you commit to a tool or a generated module, detect what it can actually do. If a component library lacks native mobile drawer support, do not let the AI generate a full navigation scheme that assumes it exists. The system should degrade gracefully instead of breaking when a capability is missing. Know the boundaries first. Design inside them.
Public engine, private values. Use a public engine for the heavy lifting, but inject your private data, configs, and conventions at runtime. This keeps your personal or company methods safe and separate from the generated scaffold. The AI builds the frame. You snap in the glass. This prevents the model from hard-coding assumptions that violate your actual standards or leak sensitive patterns into public training contexts.
Propose, do not auto-advance. Let the AI suggest the next step, but force a human to make the choice. Automate the flow of execution, never the judgment. When a model auto-generates a database migration, an auth scheme, and a payment hook in one breath, you get convenience at the cost of oversight. Make the tool present the plan. Let the developer press the button.
Picking the Right Model for the Job
I tested this across different models and found a clear split in value. Cheaper models handle mechanical bulk shockingly well. They churn out boilerplate, repetitive components, and structural stubs faster than you can type. The expensive models earn their price only when they demonstrate restraint. You want the premium option when it refuses to invent fake fixes and instead flags a genuine gap. A model that hallucinates a workaround for a missing API endpoint is dangerous. A model that stops and says, "This workflow requires a webhook target that is not defined," is worth the cost. Pay for discernment, not volume.
Build Systems, Don't Chase Magic
Do not try to fix AI gaps by throwing bigger models or more prompting tricks at the problem. Fix them with a baseline. The gap is not a capacity problem. It is an expectations problem.
To stop the missing blocks, do three things.
Give the system a completeness baseline before any code is written. Make it a physical checklist that lives next to the task.
Bake your conventions into reusable parts. If you enforce your baseline through templates, lint rules, or pre-built scaffolds, the AI starts from a position of correctness rather than hoping it guesses your standards.
Automate the flow while keeping a human in control of judgment. Let the machines handle repetition. Reserve the decisions for people who understand the context.
Here is one last habit that changed how I work. If you make the same design decision seven times, stop treating it as a one-off choice. That is not repetition. That is a law. Name it. Turn it into a rule. Document it. When you codify the pattern, you remove the chance that an AI will drift away from it the eighth time.
The source for this framework and the original exploration can be found here.
If you want to trade notes with others working through the same problems, you can join the GyaanSetu learning community.