![\"Vibe](\"https:\/\/www.jamasoftware.com\/media\/2026\/06\/vibe-coding-1.png\")
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An avionics team three months into a DO-178C (Software Considerations in Airborne Systems and Equipment Certification) effort discovers that many of their high-level requirements have no linked test cases. They had been running test cycles for weeks against requirements that were never fully verified or traced. The rework pushes certification timelines back and adds weeks of schedule pressure that nobody budgeted for.<\/span><\/p>\n Now add artificial intelligence (AI)-generated code that nobody reviewed, no documented requirements behind it, and no traceability chain for an auditor to follow. The cost multiplier worsens at every downstream stage.<\/span><\/p>\n AI coding tools have produced two distinct engineering workflows. In vibe coding, natural language prompts replace formal specifications. In spec-driven development, documented requirements govern every downstream decision. For teams building products that must pass audits, certification reviews, and safety checks, differences among these requirements affect cost, rework, and delivery risk.<\/span><\/p>\n This article defines both approaches, compares how they behave across an engineering program, and shows when each one fits.<\/span><\/p>\n Vibe coding is a workflow in which a developer prompts an AI model in natural language, accepts the generated output with minimal review, and iterates on errors rather than designing around them. The<\/span> original description of vibe coding<\/span><\/a>, coined by Andrej Karpathy, characterized it as giving in to the vibes and forgetting that the code even exists, including accepting AI output without reading diffs and pasting error messages back into the model with no comment until a bug goes away. Code outgrows the developer’s comprehension, the AI’s context window sees only fragments of the system at any moment, and the intent behind each design choice disappears once the prompt is gone.<\/span><\/p>\n The approach<\/span> can speed up software creation<\/span><\/a> while skipping core engineering practices. Code review is limited, tests are sparse, and teams trust AI output without enough checking. That distinguishes it from the broader category of AI-assisted engineering, where generated code still passes through review, testing, and traceability.<\/span><\/p>\n Spec-driven development<\/a> establishes a formal, documented specification of what to build and how to build it before design or implementation begins, and every downstream activity traces back to that specification. Standards bodies and systems engineering guidance codify this discipline. In regulated industries, requirements communicate what systems engineers expect, define project success criteria, and support regulatory conformity. A specification document is a technical artifact, a compliance instrument, and an audit trail all in one.<\/span><\/p>\n The two approaches diverge across four dimensions that matter to any engineering program. They handle requirements differently, behave differently as a system grows, generate defects at different points, and serve different kinds of teams. Looking at them side by side shows where the methods overlap and where they pull in opposite directions.<\/span><\/p>\n In vibe coding, requirements are implicit and emergent. The prompt becomes obsolete the moment code is produced, so the code itself turns into the only record of what the software does. Code is a poor medium for capturing why a decision was made.<\/span><\/p>\n In spec-driven development, the specification is the source of truth. It establishes what constitutes correctness before any implementation decisions are made. Detailed requirements covering usability, security, performance, and edge cases are written before development begins.<\/span><\/p>\n Vibe coding shortens the path from idea to working prototype. The problems surface as a project grows. The codebase exceeds anyone’s ability to hold in memory. The AI’s context window sees only fragments of the system, and no architectural map exists to support recovery when the build becomes unstable.<\/span><\/p>\n Spec-driven development starts more slowly because writing and reviewing specifications takes time. The specification then provides a stable reference that lets multiple contributors work from shared ground truth without renegotiating intent at every step. That stability compounds as team size and system complexity increase.<\/span><\/p>\n On large aerospace and spacecraft programs, a requirements error costs<\/span> three to eight times more to fix<\/span><\/a> at the design phase than at the requirements phase, and 29 times more at the operations phase. Early<\/span> requirements review<\/span><\/a> can remove issues before the first test case runs. Vibe coding bypasses that review stage, so ambiguities and omissions travel downstream until verification or an audit exposes them. Spec-driven development puts more effort up front and catches those problems at the lowest-cost point in the lifecycle.<\/span><\/p>\n Vibe coding fits solo developers, early-stage founders, and teams building disposable prototypes where the code will not face an audit, ongoing maintenance, or multi-team coordination. Spec-driven development is well-suited to multi-team programs, regulated product development, and projects that require audit evidence or long-term maintainability.<\/span><\/p>\n Vibe coding fits specific, bounded contexts. Its limits become clearer as scope and team size increase.<\/span><\/p>\n AI-assisted generation shortens iteration cycles when code is not intended for production. Its purpose in this context is to quickly validate a concept, not to serve as a maintained system, so the absence of formal specifications carries little long-term cost.<\/span><\/p>\n A solo founder validating a business idea can reach working software faster than their own skills alone would permit. Hackathon builds and one-off automation scripts fit here as well. The productivity gains apply directly, and maintainability risks matter less because the work was never meant to be maintained.<\/span><\/p>\n Teams run into production issues with AI-generated code, including performance problems, security gaps, and maintainability issues. Without deliberate design patterns or modular architecture, applications built through vibe coding struggle as user demand or feature scope grows. Inconsistent prompting styles among engineers can also lead to duplicate work and incompatible patterns within the same codebase.<\/span><\/p>\n Shared specifications reduce coordination failures that arise as programs become more complex.<\/span><\/p>\n Large engineering programs face a coordination failure that grows nonlinearly with team size. When two engineers read the same requirement differently, divergent implementations follow.<\/span> Bidirectional traceability<\/span><\/a> back to top-level product requirements, with managed changes to established baselines across the full lifecycle, is the standard approach on large programs. A shared specification gives teams, suppliers, and reviewers one authoritative reference.<\/span><\/p>\n Program requirements in regulated environments must map back to higher-level requirements and forward into verification activities. Defining verification requirements early helps control overall program cost on major defense and aerospace programs, where late discovery of a verification gap can ripple through thousands of artifacts.<\/span><\/p>\n On programs that span years or decades, original engineers routinely rotate off. Training new team members in established requirements management procedures is a core activity on long-running programs. New contributors read the specifications to understand what the program is supposed to do and why.<\/span><\/p>\n In regulated environments, the absence of formal specifications creates compliance gaps that surface during audits, certification reviews, and post-market investigations. The cost of finding those gaps late compounds across requirements, design, verification, and submission artifacts.<\/span><\/p>\n Every major safety standard governing software in MedTech,<\/span> aerospace<\/span><\/a>, and automotive depends on bidirectional requirements traceability.<\/span> DO-178C requires traceability<\/span><\/a> between requirements, source code, and test cases, with low-level requirements traced to source code and verification performed through requirements-based testing. International Electrotechnical Commission (IEC) 62304<\/span> requires traceability<\/span><\/a> from user needs and risk analysis through requirements, code, and tests. AI’s probabilistic nature and limited transparency create tension with the International Organization for Standardization (ISO) 26262<\/span> expectations for bidirectional traceability<\/span><\/a> and safety assurance throughout the development chain. Under IEC 62304, AI-generated code without formal specifications meets the functional definition of Software of Unknown Provenance (SOUP).<\/span><\/p>\n AI-generated code can introduce exploitable bugs and higher-risk architectural or privilege-related issues even when surface-level syntax errors decline. Without formal specifications and the verification and traceability activities that DO-178C and IEC 62304 expect, those architectural defects may never surface before release.<\/span><\/p>\n Without a specification, defects of omission such as missing safety behaviors, unhandled edge cases, and absent error handling are harder to detect because no document exists to measure the code against. In regulated industries, a defect found during Food and Drug Administration (FDA) premarket review or a DO-178C certification audit requires more than a code fix. It requires updated requirements, updated design documentation, updated test cases, independent re-review, and re-submission.<\/span><\/p>\n The right approach depends on regulatory constraints, team size, and what happens to the code after it is written.<\/span><\/p>\n Regulatory constraints, multi-team interfaces, and the need to trace AI-generated artifacts can each force a formal specification phase. Exploratory work has to formalize into traceable specifications at a defined transition point, and a requirements management system becomes necessary at that stage.<\/span><\/p>\n A<\/span> systems engineering<\/span><\/a> approach applies formal discipline to speed alignment during requirements analysis while working alongside formal requirements development. Artifacts from exploratory work undergo a deliberate transition that includes requirements decomposition, traceability, and security review before reaching production. The same discipline applies when AI sits within the toolchain itself, which is the rationale behind<\/span> an AI-native requirements platform<\/span><\/a> that keeps generated artifacts traceable, rather than treating AI as a bolt-on.<\/span><\/p>\n Jama Connect\u00ae<\/span><\/a> provides the Product Context Layer for engineering organizations, a governed system of record that connects requirements, risks, tests, SysML models, code repositories, simulations, defects, reviews, approvals, verification evidence, and change history. At the center sits the Traceability Information Model\u2122, which defines how requirements, models, code, tests, simulations, and verification results relate to one another.\u00a0<\/span><\/p>\n That foundation gives teams the structure to move exploratory work into production without losing the traceability links auditors expect. Jama Connect is web-based, supports on-premises deployment, and serves complex, regulated product development. Live Traceability\u2122 maintains real-time upstream and downstream visibility across development artifacts, closing <\/span>AI-related audit gaps<\/span><\/a> that arise when AI-generated code lacks documented requirements. This product context is what lets anything performed with AI stay versioned and documented as AI-generated, the discipline detailed in<\/span> spec-driven development<\/span><\/a> for AI-powered engineering.<\/span><\/p>\n Jama Connect Advisor\u2122 scores requirements against International Council on Systems Engineering (INCOSE) rules and <\/span>Easy Approach to Requirements Syntax<\/span><\/a> (EARS) patterns at the point of authoring, catching ambiguity before it reaches downstream work. Global healthcare leader Grifols <\/span>cut its review cycles<\/span><\/a> from 90 days to fewer than 30 days after bringing Jama Connect Review Center into the workflow. This demonstrates how much faster specification discipline can run when the right tooling supports it.<\/span><\/p>\n The line between vibe coding and spec-driven development appears the moment informal exploration has to become documented, traceable, and verifiable. For teams building products that will face auditors, certification authorities, or safety investigations, the specification is the compliance evidence, the onboarding document, the change-control anchor, and the institutional memory that outlasts personnel turnover. AI-assisted prototyping shortens the early stages of product development, and the engineering discipline that follows determines whether those prototypes become certified products or result in expensive rework.<\/span><\/p>\n Jama Connect supports this workflow by holding the specification as the source of truth and keeping every requirement, design artifact, and test linked as the work moves from exploration into production. Start a<\/span> free 30-day trial of Jama Connect<\/span><\/a>.<\/span><\/p>\nWhat Is Vibe Coding?<\/b><\/h2>\n
What Is Spec-Driven Development?<\/b><\/h2>\n
How Vibe Coding and Spec-Driven Development Differ<\/b><\/h2>\n
How Each Approach Handles Requirements<\/b><\/h3>\n
Initial Output Speed vs Speed at Scale<\/b><\/h3>\n
Where Do Defects and Rework Originate?<\/b><\/h3>\n
Who Does Each Workflow Serve Best?<\/b><\/h3>\n
See how engineering leaders are using spec-driven development to remove bottlenecks and increase product velocity.<\/span>
\nDownload the Spec-Driven Development Playbook \u2192<\/em>
\n<\/a><\/div>\nWhen to Use Vibe Coding<\/b><\/h2>\n
Why Prototyping Is the Strongest Use Case<\/b><\/h3>\n
Where Do Solo Projects and Disposable Code Fit?<\/b><\/h3>\n
What Limits Surface as Teams Grow?<\/b><\/h3>\n
Why Spec-Driven Development Holds Up as Programs Grow<\/b><\/h2>\n
How Shared Specifications Reduce Ambiguity<\/b><\/h3>\n
How Verification Maps Back to Defined Intent<\/b><\/h3>\n
Why Onboarding and Handoffs Stay Predictable<\/b><\/h3>\n
Where Vibe Coding Creates the Most Risk in Regulated Systems<\/b><\/h2>\n
How Untraceable Decisions Create Audit Gaps<\/b><\/h3>\n
AI-Generated Code Without Specs Expands the Defect Surface<\/b><\/h3>\n
What Late-Stage Requirement Discovery Costs<\/b><\/h3>\n
How to Choose the Right Approach for Your Team<\/b><\/h2>\n
How to Match Method to Project Complexity<\/b><\/h3>\n
How to Blend Exploration With Specification Discipline<\/b><\/h3>\n
How Jama Connect Supports Spec-Driven Development<\/b><\/h2>\n
Choosing Between Vibe Coding and Spec-Driven Development for Your Program<\/b>\u00a0<\/span><\/h2>\n