Code Generation is About Control, Not Prompts

The AI coding assistant market in 2025 is obsessed with the wrong things. Companies compete on context window sizes, prompt engineering techniques, and sophisticated memory systems. They tout their agents’ ability to “understand” your codebase and “intelligently” decide what to do next.

But here’s the truth: the best way to get reliable code from an LLM isn’t about better prompting. It’s better control and better enforcement.

The Prompt Engineering Trap

Current AI coding tools—Cursor, Windsurf, GitHub Copilot, and dozens of others—fundamentally approach code generation the same way: give the LLM maximum context, craft the perfect prompt, and hope it does the right thing.

Cursor’s Agent mode can “autonomously search for relevant code snippets, open files that need editing, generate modification plans, and even run tests.” Windsurf promises its Cascade assistant can “seamlessly switch between answering questions and autonomously executing multi-step tasks.” The industry has embraced agentic AI: systems where LLMs dynamically direct their own processes and tool usage.

This sounds impressive until you ship to production.

The problem isn’t that LLMs are bad at code generation—they’re remarkably good. The problem is that autonomous decision-making compounds errors exponentially. An LLM might:

Skip validation steps it deems unnecessary
Generate tests that pass but don’t actually test the right behavior
Refactor code in ways that break subtle invariants
Drift away from architectural patterns as context accumulates
Hallucinate workflows that seem reasonable but violate your domain constraints

Recent studies show a correlation between widespread LLM adoption and decreased stability in software releases. The tools that promise to make us more productive are making our software worse because they optimize for the wrong thing: giving the LLM freedom instead of giving it structure.

Control vs. Suggestions

Traditional AI coding tools work through suggestion:

“Here’s my codebase. Here’s the feature I want. Please write good code and tests.”

The LLM then decides:

What files to read
What order to do things
Whether to run tests
How to handle failures
When it’s “done”

Some tools add guardrails—safety checks after the fact, sandboxed execution environments, human review gates. But these are defensive measures against an adversarial problem. You’re trying to catch the LLM when it goes off the rails.

Control-based systems work differently. They don’t try to prevent the LLM from making bad decisions—they prevent it from making certain decisions at all.

Enforcement Architecture

We build on a radically different premise: code generation should be a constrained, procedural workflow, not an autonomous agent.

Here’s what that means in practice:

1. Predefined Step Sequences

Instead of letting the LLM decide what to do next, define explicit workflows. For example, a component design workflow might look like:

Initialize → Generate → Validate → Revise (if needed) → Finalize

The LLM cannot skip validation. It cannot decide tests are optional. An orchestrator enforces the sequence through explicit state transitions. If validation fails, the only next step is revision—no exceptions, no autonomy.

2. Step Contracts

Each step implements a strict interface with two key responsibilities:

Command Generation: Define exactly what command to execute
Result Handling: Validate and process the output

Each step must explicitly update session state and cannot proceed without successful validation. This isn’t “prompt engineering.” It’s interface enforcement. The LLM doesn’t get to decide how to handle results—your code does.

3. Structural Validation Between Steps

Don’t ask the LLM to “make sure it’s valid.” Parse its output against a required structure. For example, if the LLM generates a design document, validate it has all required sections before proceeding.

If validation fails, the system transitions to a revision step with explicit error details. The LLM doesn’t get to explain why the missing section doesn’t matter—the system rejects it and forces correction.

4. Test-Driven Enforcement

Make testing a mandatory workflow step, not an LLM suggestion:

Generate → RunTests → FixFailures (if needed) → RunTests again

Execute actual tests and parse the output. If tests fail, automatically trigger a fix step with the exact failure messages. Loop until tests pass or a human intervenes.

The LLM cannot decide “the tests are flaky” or “this failure doesn’t matter.” Tests pass or the workflow doesn’t complete.

5. Stateless Orchestration

Traditional AI assistants maintain conversational state—the LLM’s context window accumulates history, and its decisions are influenced by everything that came before. This creates context drift: the LLM gradually moves away from your original intent as the conversation evolves.

Instead, treat each interaction as:

Created with explicit scope and context
Executed as a discrete command
Validated independently
Stored as an immutable record

The next step gets the current session state, not a conversation history. The orchestrator determines what happens next based on the result status, not LLM interpretation.

6. Scoped Isolation

Require explicit scope for all operations. Sessions should only access data within defined boundaries—a UI component session shouldn’t read authentication code, for example.

This isolation prevents cross-contamination and limits blast radius. The LLM can’t “helpfully” wander into unrelated parts of your codebase.

What This Prevents

This control-based architecture eliminates entire classes of LLM failures:

Hallucinated Workflows: The LLM cannot invent process steps. The orchestrator defines all possible state transitions.

Validation Bypass: Structural validation happens between steps, enforced by code, not prompts.

Context Drift: State is explicit and scoped, not accumulated in conversation history.

Test Avoidance: Tests are mandatory steps in the workflow, not LLM suggestions.

Unbounded Generation: Each step has specific input/output contracts. The LLM generates within those bounds or the interaction fails.

Inconsistent Patterns: Architectural patterns are encoded in the step implementations and validation logic, not described in prompts.

The Cost of Control

This approach isn’t free. Control-based systems require:

More Upfront Design: You must define your workflows, validation rules, and step sequences before the LLM runs.

Less Flexibility: The LLM cannot creatively adapt to unexpected situations. If something doesn’t fit your workflow, you need a human.

Infrastructure Complexity: Orchestrators, step behaviors, result handlers—these are sophisticated systems.

Perceived Slowness: Validation steps and mandatory testing add latency.

But here’s what you get in return: predictability. When a control-based workflow completes, you know:

Every required validation passed
All tests executed and passed
The design conforms to required structure
State transitions followed the defined workflow
You have a complete audit trail of what happened

The Industry Will Follow

Right now, the AI coding tool market is in its “agent maximalism” phase. Every company is racing to give their LLM more autonomy, more tools, more freedom to “think” and “decide.”

This is a dead end.

As more teams ship LLM-generated code to production, they’ll discover what some of us are already learning: autonomy without constraints is technical debt with a faster commit rate.

The tools that win won’t be the ones with the best prompts or the biggest context windows. They’ll be the ones with the best enforcement architectures—systems that use LLMs as powerful code generators within rigidly controlled workflows.

Prompt engineering is about asking nicely. Control is about making it impossible to do the wrong thing.

What This Means for You

If you’re building with AI coding assistants today:

Stop optimizing prompts. They’ll always be fragile. Instead, ask: what workflow constraints would make bad outputs impossible?

Stop adding more context. LLMs already have enough context to generate bad code confidently. Add structural validation between generation steps.

Stop treating the LLM as an agent. Treat it as a powerful, unreliable code generator that needs adult supervision at every step.

Start building enforcement architectures. Define your workflows explicitly. Make your validation steps mandatory. Create state machines that cannot be bypassed.

The future of AI-assisted development isn’t smarter agents. It’s better cages.