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1. Core Activities & Roles (Development)

Purpose

Overview of core activities and role assignments during the Development phase, from data automation to model development and test validation.

1. Core Activities

Automating Data Flows

Setting up pipelines that automatically clean and supply data (no more manual work).

  • Data Pipelines: Automated ETL processes (Extract, Transform, Load)
  • Quality Controls: Automatic validation of incoming data
  • Version Control: Tracking of data changes and lineage

Knowledge Coupling & Fine-Tuning

Connecting the AI to internal documents and model fine-tuning for optimal performance.

  • Knowledge Coupling: Connecting the AI to internal documents, FAQs, procedures. Like this whole ai-delivery.io blueprint
  • Prompt Engineering: Optimising the Steering Instructions.
  • Model Fine-Tuning: Adjusting parameters for the specific use case.

Specification-First Method

We write the expected outcome (the test) first, then the implementation. This ensures quality.

  • Test-Driven Development for AI: First define what the system must do.
  • Acceptance Criteria: Clear, measurable requirements per feature.
  • Automated Tests: Continuous validation with every change.

Variant: SaaS & Procurement (Buy vs. Build)

Not all AI solutions are built in-house. When purchasing standard AI software (SaaS), the focus of the Development phase changes:

  • From Building to Configuring: Focus on setting up the right system prompts, knowledge coupling sources and safety filters within the vendor environment.
  • Validation Remains Identical: Even a purchased tool must pass the Validation Pilot and Golden Set test before going live. Do not blindly trust the vendor's "demo".
  • Model Card becomes Configuration Card: Document which settings, plugins and data connections are active.
  • Vendor Lock-in Check: Verify that data and logs are exportable for compliance (EU AI Act).

Validation at Three Levels

Every change is tested on three dimensions:

Syntactic

  • Question: Does the code work? No crashes or errors?
  • Check: Unit tests, integration tests

Technical Delivery & Pipelines

  • Data Pipelines: Setting up robust flows for training and inference.
  • Automated Gates (Governance-as-Code): Integrate the Hard Boundaries and success metrics directly into the CI/CD pipeline.
  • Example: The build automatically fails if the bias score is too high or accuracy drops below the threshold.
  • Continuous Testing (CT): Automated evaluation of model outputs with every change to the Steering Instructions.

Behavioural

  • Question: Does it do what we expect?
  • Check: Functional tests, regression tests

Goal-Aligned

  • Question: Does it help the user? Does it deliver value?
  • Check: User acceptance testing, A/B testing

2. Team & Roles

Role Responsibility in Development
Data Scientist Responsible: Development of AI models and Knowledge Coupling.
ML Engineer Responsible: Building data pipelines and infrastructure.
AI Product Manager Accountable: Owner of the product backlog and prioritisation.
QA Engineer Responsible: Performing automated tests and validation.
DevOps Consulted: Advises on Go-live and infrastructure.

Templates:

Further reading:

See also: Phase 3 Overview · Deliverables

Next step: Start the SDD cycle: write the spec, derive the Golden Set, build and validate. → Use the Technical Model Card as your starting point. → See also: SDD Pattern | Validation Report