A Step-by-Step Guide to Specification-First AI Development

Imagine a healthcare AI system classifying benign tumors as malignant, triggering unnecessary treatments because developers didn't clearly specify acceptable false-positive rates.

The rising failure rate of AI initiatives tells a clear story: without proper specs, even the most sophisticated models falter. While teams rush to code, adopting a specification-first approach to AI development ensures that technical capabilities align with business objectives from the outset.

This article explores how to implement spec-first AI development to create more reliable, compliant, and business-aligned AI systems—saving time, reducing costs, and delivering results that actually solve the intended problems.

What is Specification-First AI Development?

Specification-first (or spec first) AI development is a methodical approach that prioritizes comprehensive documentation of system requirements, performance expectations, and compliance needs before any code for the AI system is written.

Unlike code-first methodologies, where development begins with minimal requirements and evolves through iteration, specification-first establishes clear success criteria and boundaries upfront, creating a foundation for evaluation.

Specifications serve as a single source of truth across diverse stakeholders—from technical teams to business leaders, compliance officers, and domain experts. They document not just functional requirements but expected behaviors, performance thresholds, ethical boundaries, and compliance needs. 

This shared understanding bridges the often substantial gap between technical capabilities and business requirements.

Properly constructed specifications transform abstract objectives like "build an accurate model" into concrete, measurable success criteria that guide development and evaluation. This clarity enables more efficient development cycles and creates accountability across teams by establishing objective standards for success.

Key Benefits of Spec-First AI Development

• Enhanced Stakeholder Alignment: Specifications create a shared understanding, or AI fluency, between technical and business teams, eliminating ambiguity about project goals and success criteria.

• Earlier Problem Detection: Addressing inconsistencies and potential issues during specification saves substantial resources compared to discovering them during development or deployment.

• Improved Collaboration Across Disciplines: Specifications provide a common language for cross-functional teams to communicate requirements and constraints effectively. When legal, compliance, engineering, and product teams collaborate on specifications, they establish clear boundaries that respect both technical limitations and regulatory requirements, preventing siloed decision-making.

• Reduced Development Cycles: Clear specifications minimize rework by establishing precise expectations before development begins. Organizations implementing specification-first approaches get to experience reductions in iteration cycles as engineers spend less time guessing what stakeholders want and more time building to defined standards.

• Built-in Compliance and Risk Management: Specifications incorporate regulatory requirements from the beginning, making compliance an integral part of development rather than an afterthought. By embedding regulatory guidelines directly into model specifications, such as complying with the EU AI Act, organizations ensure AI systems align with relevant regulations throughout development.

• Objective Evaluation Frameworks: Specifications establish concrete, measurable success criteria that enable objective assessment of AI system performance. This transforms subjective judgments like "the system should be accurate" into quantifiable metrics like "the system must achieve 95% precision on critical classifications with no more than 2% false positives." Utilizing tools like leaderboards for evaluating AI agents can further enhance this objective assessment by providing benchmarks against industry standards.

How Specification-First Borrows from TDD's Philosophy

If you're familiar with test-driven development, the core idea here will feel familiar: define what success looks like before you build. 

TDD's "write tests first, then code" becomes "write specs and evals first, then build your AI." The philosophy is the same—flipping the traditional build-then-verify sequence. 

But the implementation diverges significantly because you're not writing unit tests for deterministic functions; you're creating evaluation frameworks for probabilistic models. The connection is real enough that TDD practitioners often grasp spec-first AI quickly, but it's more intellectual precedent than direct application. 

You don't need to know TDD to practice specification-first development. It just helps frame why this approach feels right to anyone who's seen the value of defining expected behavior upfront.

How to Implement Spec-First AI Development

Here’s how you can implement spec-first AI development in your organization.

Step #1: Define Business Objectives and AI Capabilities

You'll want to get crystal clear on what problem you're actually solving—not what your stakeholders think they want built. It helps to document hard boundaries between in-scope and out-of-scope early, because scope creep kills AI projects faster than bad data. 

Frameworks like Job Stories work well for capturing outcomes rather than features, and concrete acceptance criteria leave zero room for "it depends" conversations later. An AI capabilities map shows exactly how technical reality aligns with business ambitions, which saves painful realizations down the line.

Step #2: Create Detailed Input and Output Specifications

This is where vague requirements come back to haunt you. You'll need to define exactly what your system can handle: file formats, data structures, value ranges, edge cases—all of it. 

For outputs, the same level of specificity applies to formats, quality thresholds, and confidence scores. Creating contrast tables that show the difference between "professional-sounding" (useless) and "maintains formal tone with Flesch score 40-60" (actionable) makes a huge difference. 

It's also worth spelling out when your system should gracefully bow out and escalate to humans rather than hallucinating with confidence.

Step #3: Establish Quantifiable Performance Metrics

You'll want metrics that actually matter for your use case, not just what's easy to measure. Classification problems need precision and recall, but generative AI needs coherence and factual accuracy metrics. 

Tracking both technical performance and business impact matters—your model might be 95% accurate, but if users hate it, you've built the wrong thing. 

Turning fuzzy requirements like "natural-sounding" into concrete rubrics helps your team evaluate consistently, eliminating the dreaded "looks good to me" approval process.

Step #4: Propose Ethical Guidelines and Safety Guardrails

It's better not to wait until your model says something embarrassing in production to think about safety. Running systematic risk assessments early helps surface bias, privacy issues, and potential harms. 

The key is transforming vague ethics like "be fair" into testable requirements: "demographic parity within 5% across protected groups." 

Building in content filters, action boundaries, and human oversight from day one saves massive headaches. Retrofitting ethics after launch is exponentially harder than baking them into your specifications upfront—most teams learn this the hard way.

Step #5: Conduct Cross-Functional Spec Reviews

Getting everyone in a room before anyone writes code catches issues early. Pulling in engineers, domain experts, compliance, and actual end users helps pressure-test your specs. 

The ACME framework (Assumptions, Constraints, Metrics, Examples) works well for systematically finding gaps. Scenario role-plays where people interact with your imaginary system reveal requirements conflicts fast. Checklists covering completeness, consistency, and feasibility help too. 

These sessions might feel tedious until they catch the million-dollar oversight nobody else spotted.

Step #6: Develop Test Cases from Specifications

A traceability matrix linking every spec to its test cases becomes invaluable when requirements change (they will)—you'll know exactly what to update. Test datasets should cover standard cases, edge cases, and adversarial examples that stress your system's limits. 

Layering your testing helps: unit tests for components, integration for workflows, specialized tests for fairness and statistical properties. "Red team" adversarial tests that deliberately try to break your safety guardrails are particularly revealing. 

If your tests don't occasionally surprise you, they're probably not comprehensive enough.

Step #7: Implement Continuous Specification Refinement

Your specs will evolve, so it helps to plan for changes rather than pretending they're written in stone. Distinguishing between clarifications (low friction) and fundamental changes (needs approval) keeps things moving smoothly. 

Versioning everything and documenting why decisions were made—not just what changed—preserves institutional knowledge. A decision framework weighing implementation impact against business value prevents changes from derailing your timeline. 

Scheduling periodic spec reviews at milestones helps incorporate what you've learned. Integrated tools that keep requirements and code in sync automatically are worth investing in, because manual synchronization rarely survives contact with reality.

What to include in an AI project specification: A checklist

• Business Alignment

  • Core business problems and expected outcomes

  • In-scope vs. out-of-scope boundaries

  • Concrete acceptance criteria

  • AI capabilities map linking goals to technical requirements

• Input/Output Definitions

  • Acceptable input formats and data structures

  • Edge case and error handling procedures

  • Output formats and quality thresholds

  • Confidence score requirements and escalation triggers

• Performance Standards

  • Technical metrics (accuracy, latency, throughput)

  • Business impact metrics (user satisfaction, cost savings)

  • Baseline thresholds and improvement targets

  • Measurable rubrics for subjective criteria

• Ethics & Safety

  • Risk assessment covering bias, privacy, and security

  • Testable ethical requirements with specific thresholds

  • Content filtering and guardrail rules

  • Human oversight protocols

• Validation Framework

  • Cross-functional review schedule and participants

  • Test case traceability matrix

  • Diverse test datasets (standard, edge, adversarial)

  • Red team testing procedures

• Change Management

  • Specification versioning system

  • Change approval processes

  • Periodic review milestones

  • Requirements-to-code synchronization tools

Best Practices for Specification-First AI Development

The specification-first approach only works if your specs are structured to drive development decisions and evaluation cycles, not just document requirements after the fact. 

Here's how to make them truly operational.

Treat Specifications as Your Evaluation Source of Truth 

Your specs and eval criteria should be two views of the same thing, not separate documents that slowly diverge. Structure specifications so each requirement directly maps to evaluation metrics you'll track throughout development. 

When your specs say "responds in under 500ms for 95% of queries," that exact threshold should appear in your evaluation dashboard. This tight coupling prevents the common problem where teams build to spec but evaluate against different criteria.

Write Testable Specifications from Day One 

Every specification statement should answer "how will we know if this works?" If you can't imagine a test that verifies a requirement, it's probably too vague. 

Instead of "system should handle edge cases gracefully," write "system returns structured error messages for malformed inputs and logs them for analysis." 

This discipline forces clarity upfront and eliminates the scramble to figure out what "done" means when you're trying to ship.

Start with Minimal Viable Specifications 

You don't need perfect specs to begin—you need enough to start building and evaluating. Focus first on core functionality, critical performance thresholds, and non-negotiable safety requirements. 

Add detail iteratively as you learn from implementation. Over-specifying upfront often means investing effort in areas that turn out not to matter, while under-specifying the wrong things causes painful rewrites. 

Finding this balance comes from understanding your risk profile and being honest about uncertainty.

Align Specifications with Your Evaluation Cadence 

If you're running evals weekly, your specs need to support that rhythm. Structure requirements so they're measurable at each evaluation checkpoint, not just at final delivery. This means breaking large specifications into smaller, independently verifiable components. 

You'll catch issues faster when specs align with how you actually assess progress, rather than discovering problems only during major milestones when course corrections are expensive.

Make Specifications Executable, Not Just Readable 

The best specs can be programmatically validated, creating automated feedback loops. Use structured formats that evaluation frameworks can parse directly—JSON schemas for data specs, threshold values that feed into monitoring dashboards, constraint definitions that become guardrail tests. 

This doesn't mean abandoning prose explanations, but augmenting them with machine-readable components. 

When specs are executable, your CI/CD pipeline becomes an enforcer of requirements, not just a deployment mechanism.

Use Specifications to Prevent Feature Creep 

Upfront specs give you a polite way to say no. When stakeholders request additions mid-project, you can point to defined success criteria and ask: does this align with our specifications, or is this a different project? 

This doesn't mean being inflexible—legitimate improvements should update specs through your change management process. 

But it creates accountability and forces explicit decisions about scope changes rather than gradually accumulating requirements that derail timelines and dilute focus.

Evaluate Your AI Development Process With Galileo

Specification-first development transforms AI initiatives from uncertain experiments into predictable, measurable projects with clear success criteria. Galileo enables teams to build more robust, compliant, and business-aligned AI solutions. Here are five key ways Galileo supports your specification-first AI development approach:

• Collaborative Specification Design: Galileo provides a centralized platform for cross-functional teams to collaboratively define and refine AI specifications, ensuring alignment between technical capabilities and business objectives.

• Automated Metric Generation: Based on your specifications, Galileo automatically generates relevant evaluation metrics, saving time and ensuring comprehensive coverage of performance criteria.

• Continuous Evaluation Framework: Galileo enables ongoing assessment of AI models against defined specifications, allowing teams to track progress and identify potential issues throughout the development lifecycle.

• Compliance Guardrails: Built-in tools help teams incorporate ethical guidelines and regulatory requirements directly into AI specifications, ensuring compliance is baked in from the start.

• Traceability and Reporting: Galileo maintains a clear link between specifications and evaluation results, providing auditable trails and facilitating communication with stakeholders.

Explore how Galileo can streamline your workflow, enhance your specification-first AI development process, and improve your AI outcomes.