Spec‑as‑Code: Why Detailed Specifications Aren’t a Shortcut

Why the “spec‑as‑code” hype needs a reality check

The promise of generating production‑ready software from a well‑written document sounds like a developer’s dream. Yet a recent article on Haskellforall pulls back the curtain on the myth, showing that specifications often become thinly veiled code, and that the “automation” they enable is far from reliable.

Image: UBOS AI platform visualizing the bridge between specs and generated code.

Key takeaways from the Haskellforall post

• The author identifies two pervasive misconceptions: (1) specs are simpler than the code they replace, and (2) writing specs is inherently more thoughtful than coding.
• OpenAI’s Symphony project is used as a concrete case study. Its “SPEC.md” reads more like pseudocode than a high‑level design.
• Attempts to generate Symphony from the spec using Claude Code resulted in broken builds, missing functionality, and endless retries.
• The post argues that any spec detailed enough to drive reliable code generation inevitably collapses into a code‑like artifact, negating the supposed productivity gain.

Misconception #1: Specs are simpler than the resulting code

The belief that a natural‑language document is easier to author than a full implementation ignores the hidden complexity of precision. To be unambiguous, a spec must encode data structures, control flow, and error handling—elements that are native to code. As Dijkstra famously warned, “narrow interfaces” (i.e., code) cannot be replaced by vague prose without increasing the overall workload.

Why precision forces code‑like specs

1. Formal semantics: Every API contract, state transition, and concurrency rule must be expressed in a deterministic way.
2. Tooling expectations: AI agents parse markdown, JSON schemas, or annotated text; without a strict format they cannot infer intent.
3. Maintenance overhead: When the system evolves, the spec must be updated with the same rigor as the source code.

The result is a document that looks and feels like code, eroding the supposed simplicity advantage.

Misconception #2: Spec writing is inherently more thoughtful than coding

In fast‑paced product environments, teams often prioritize delivery speed over deep analysis. When a spec is treated as a “quick‑write” artifact, it becomes a collection of shallow bullet points or, worse, AI‑generated filler. The Haskellforall article highlights sections of Symphony’s spec that read like “slop” – disjointed sentences lacking coherent purpose.

The hidden cost of rushed specs

• Ambiguities that later surface as bugs.
• Redundant or contradictory statements that confuse both humans and agents.
• Increased rework when the generated code fails to meet real‑world expectations.

In short, a hurried spec can be more error‑prone than writing the code directly.

Symphony: A cautionary tale of spec‑driven generation

OpenAI’s Symphony project claims to be an “agent orchestrator” built from a single markdown specification. The spec includes:

4.1.6 Live Session (Agent Session Metadata)
Fields: session_id (string), thread_id (string), codex_app_server_pid (string or null), …
    

and even concrete code snippets such as:

function start_service():
    configure_logging()
    start_observability_outputs()
    …
    

When the author fed the spec to Claude Code, the generated Haskell version compiled with errors, required manual patches, and stalled on a simple “create a blank git repository” task. The experience mirrors a broader pattern: the more detailed the spec, the more it morphs into a pseudo‑implementation, and the less the AI can add genuine value.

What this means for modern developers

The fallout from the Symphony experiment offers several actionable insights:

• Don’t treat specs as a shortcut. Use them to clarify intent, not to replace the coding effort.
• Invest in high‑quality documentation. Pair specs with diagrams, examples, and test suites rather than raw markdown tables.
• Leverage AI as an assistant, not a replacement. Prompt agents to suggest refactorings, generate boilerplate, or write unit tests, while keeping a human in the loop for architectural decisions.
• Adopt a “spec‑plus‑code” workflow. Write a concise spec, then iterate on the generated code, feeding back improvements into the spec.

Companies that recognize these limits can still reap AI benefits—speed, consistency, and reduced rote work—without falling into the trap of over‑promising on fully automated development.

Conclusion: Specs are code, but they’re not a magic wand

The Haskellforall analysis makes it clear: a “sufficiently detailed” specification is, by definition, code‑like. Expecting an AI to conjure a flawless implementation from such a document is unrealistic. Instead, treat specifications as living artifacts that guide, document, and validate the code you write.

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