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September 22, 2026

Preventing AI Coding Mistakes: Proofs vs. Planning

Code generation models are powerful but introduce subtle, costly errors. We explore two strategies for preventing AI coding mistakes: formal verification (proofs) and structured planning. While proofs offer guarantees, we argue that rigorous, upfront planning is the more practical and scalable approach for most development teams.

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AIArchitecturePlanningSoftware DesignCode Generation
HyunKi··6 min read
Preventing AI Coding Mistakes: Proofs vs. Planning

Preventing AI Coding Mistakes: Proofs vs. Planning

The acceleration of software development via code generation is real. But so is the new class of bugs that comes with it. These are not simple syntax errors, but plausible, subtle mistakes in logic that pass basic tests and fail under edge conditions. The highest-leverage work is therefore not in generating code faster, but in preventing AI coding mistakes from the start. The central question is how to build effective guardrails for a non-deterministic system.

Two dominant philosophies are emerging. The first is the path of mathematical certainty: formal verification, or proofs. The second is the path of structured direction: rigorous, upfront planning. While proofs have their place in safety-critical systems, we believe that for the vast majority of product development, planning is the more effective and scalable discipline. It treats the specification not as a suggestion, but as the primary tool for constraining the solution space.

The Allure and Risk of Generated Code

Large language models can produce hundreds of lines of functional code in seconds. This is a significant shift in developer workflow. The initial output for a new component or API endpoint appears complete, handling the happy path with correct syntax. It feels like a massive productivity gain.

The risk lies in the long tail of behavior. The model might generate a data-processing function that works for small inputs but has quadratic complexity, failing catastrophically under load. It might implement an authentication check that seems secure but misses a specific token validation step. These errors are difficult to catch because the code looks correct. It is syntactically valid and often follows familiar patterns, lulling the reviewer into a false sense of security. The cost of these bugs, discovered late in the cycle or in production, negates the initial speed advantage.

The Path of Proofs: Formal Verification

One response to this challenge is to demand mathematical guarantees. Formal verification methods use tools like TLA+, Alloy, or Coq to model a system's behavior and prove that it adheres to a set of invariants. For example, you could prove that a distributed database protocol can never lose data during a network partition or that a state machine can never enter a deadlock state.

This approach is powerful. For mission-critical infrastructure—avionics, medical devices, financial ledgers—the cost of failure is so high that the cost of formal proof is justified. It provides the strongest possible guarantee of correctness for the properties it covers.

However, proofs are not a universal solution. They require deep specialization and significant effort to create and maintain. The process of writing a formal specification is often more time-consuming than writing the code itself. For most product teams, building features under market deadlines, the overhead of formal verification is impractical. It is a specialized tool for a narrow but critical class of problems, not a general-purpose methodology for building user-facing applications.

The Path of Planning: A Framework for Preventing AI Coding Mistakes

A more pragmatic approach is to focus on the input to the generation process: the plan. If a model produces incorrect code from a vague prompt, the solution is not to fix the code after the fact. The solution is to provide a plan so detailed and unambiguous that it dramatically narrows the possibility of error. A well-structured plan acts as a set of executable constraints.

This is the philosophy Bridge is built on. The system doesn't just take a one-line feature request. It requires a specification that defines the problem from multiple angles. By front-loading this work, you are not slowing down; you are engaging in the most important phase of execution. You are debugging the feature's logic before a single line of code exists. This detailed guidance is what enables a planner to generate code that is not just plausible, but correct.

What Makes a Good Plan?

A plan that effectively constrains a code generation system is more than a user story. It is a technical specification that anticipates ambiguity and resolves it. It defines the boundaries of the problem, including what not to do.

A minimal specification should include clear definitions for the primary goal, the user it serves, the specific features required, explicit non-goals, and any technical constraints. This structure forces clarity and provides the model with a rich, unambiguous context.

Here is a sketch of a specification for a simple feature. It is not code, but a set of instructions that guides the code to be written.

primary_goal: "Enable users to export their project data as a CSV file."
user_persona: "Project manager who needs to import data into a spreadsheet for reporting."

key_features:
  - A button in the project settings page labeled "Export as CSV".
  - The CSV must contain columns: 'task_id', 'task_name', 'status', 'due_date'.
  - The file must be named `project_{id}_export_{timestamp}.csv`.
  - The 'due_date' column must be formatted as ISO 8601 (YYYY-MM-DD).

non_goals:
  - No other export formats (e.g., JSON, PDF).
  - No UI for selecting or reordering columns.
  - The feature will not be available to read-only users.

technical_constraints:
  - Must be a streaming download to handle projects with over 10,000 tasks.
  - The server-side implementation must not block the event loop for more than 100ms.
  - Must use the existing `project_permissions` service for authorization.

This level of detail leaves little room for interpretation. It specifies column names, date formats, file naming conventions, and performance characteristics. It explicitly rules out adjacent features. This is the kind of input that allows a system to generate correct, production-ready code.

Planning as Execution

The critical insight is that this planning phase is execution. Every decision made in the specification—every non-goal defined, every technical constraint added—is a bug that will never be written. It is the highest form of leverage in software development. An hour spent refining a plan saves ten hours of debugging generated code.

This approach shifts the developer's role from writing boilerplate to designing systems and defining behavior. The creative, problem-solving work happens in the specification. The code generation system becomes a tool for realizing that well-defined vision, not a source of unpredictable output.

Formal proofs provide the ultimate guarantee of correctness, but their cost and complexity make them unsuitable for most software projects. A disciplined, rigorous planning process offers a more practical path to the same goal. By treating the plan as the primary artifact of execution, we can build systems that reliably generate correct code. This is how we move from simply making code faster to making it better.

Ready to build with a plan? See how Bridge works at bridgedev.io or join the waitlist.

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