Annex IV Technical Documentation: A Practical Guide

Why Annex IV Matters

If your AI system is classified as high-risk under the EU AI Act, you'll need to prepare technical documentation following Annex IV of the regulation. This documentation has to be ready before the system goes on the EU market or into service, and you need to keep it current throughout the system's lifecycle.

The documentation has two jobs. First, it shows authorities and notified bodies that your system meets the regulation's requirements. Second, it gives deployers (your customers) what they need to use the system safely and lawfully.

This guide walks through each section of Annex IV, explains what's actually required, and offers some practical tips along the way.

The Structure of Annex IV

Annex IV covers the areas below. All documentation must be prepared before market placement or deployment, and kept up to date.

Section 1: General Description of the AI System

Here you lay out who built the system and what it does. You need to document:

• The system's intended purpose, described clearly and specifically. This isn't a marketing blurb. It must be precise enough to determine the system's risk classification and which requirements apply.

• Provider identity: name, address, and contact details (plus authorized representative, if applicable).

• How the system is identified and versioned, including software version numbers.

• Any hardware the system is intended to run on, if relevant.

• If the system is a component of a product, how it integrates and the product's CE marking information.

• How the AI system interacts with other hardware or software, covering APIs, data flows, and system dependencies.

Practical tip: Start with your existing product specs and engineering design records. Most of this information is already somewhere in your organization; it just needs to be reorganized into the Annex IV structure.

Section 2: Detailed Description of System Elements and Development Process

This is the heaviest technical section. It covers:

• Development methods and techniques: the methodologies you used, including model architecture, training approaches, and design choices.

• Design specifications: the system's architecture, data flow, computational logic, and algorithmic principles.

• System architecture: how components interact, including model pipelines, pre-processing, post-processing, and integration layers.

• Computational resources used for development, training, testing, and validation. Include hardware specs and cloud infrastructure details.

• Third-party components: any pre-trained models, external tools, libraries, or datasets, with their versions and license terms.

• The rationale behind key design decisions, including trade-offs you considered.

Practical tip: This section needs your engineering and compliance teams working together. Set up a process that captures these details during development, not after the fact. Our document generator can auto-populate much of this from your AI system registry data.

Section 3: Monitoring, Functioning, and Control

Now we get into how the system actually behaves in operation:

• What the system can and cannot do, including known limitations, foreseeable misuse scenarios, and conditions where performance may degrade.

• Accuracy, robustness, and cybersecurity levels: performance characteristics including accuracy metrics, robustness under adversarial conditions, and cybersecurity protections (per Article 15).

• Human oversight measures (per Article 14): the tools, interfaces, and procedures that let humans monitor, intervene in, or override the system's outputs.

• Input data specifications: what data the system expects, including format, quality requirements, and constraints.

Section 4: Risk Management System Documentation

Article 9 requires a risk management system for high-risk AI. Annex IV requires you to document it, including:

• The risk management process and methodology
• Identified risks and how they were assessed
• Risk mitigation measures you implemented
• Residual risks and why they're acceptable
• Ongoing monitoring and review procedures

The key word here is "continuous." Regulators want to see an iterative process, not a one-time assessment.

Section 5: Data and Data Governance

Your data practices go here, aligned with Article 10.

For training data, describe your datasets: their origin, scope, characteristics, availability, quantity, and how the data was collected and prepared. You also need to cover data quality measures (techniques for ensuring quality, identifying biases, and addressing gaps), the datasets used for validation and testing along with how you selected them, and your data governance policies and procedures across the system lifecycle.

For AI systems processing personal data, this section must also address GDPR compliance, including the legal basis for processing and any Data Protection Impact Assessments you've conducted.

Section 6: Testing and Validation

Annex IV requires thorough documentation of your testing:

• Testing procedures: the methodologies, metrics, benchmarks, and test protocols used.

• Actual test results, including accuracy, precision, recall, and other relevant metrics.

• Performance broken down by relevant demographic or contextual subgroups, to surface potential biases or disparate impacts.

• Adversarial testing results, including robustness against attacks and edge case analysis.

• How you validated the system against its intended purpose and real-world conditions.

Section 7: Changes and Modifications

Technical documentation is a living document. You need to maintain records of:

• All changes made to the system after initial documentation
• An impact assessment for each change on the system's compliance status
• Updated test results after significant modifications
• Version history with clear traceability

Common Pitfalls and How to Avoid Them

Treating documentation as an afterthought

The most common mistake is trying to write Annex IV documentation after the system is already developed and deployed. You end up with gaps, inconsistencies, and a painful scramble to reconstruct information. The fix is straightforward: build documentation into your development process and capture information as you go.

Insufficient detail on data governance

Regulators will look closely at your data practices, especially around bias and representativeness. Vague statements like "we use high-quality data" won't pass. Document specific data sources, collection methods, preprocessing steps, quality metrics, and bias assessments.

Missing performance disaggregation

Reporting overall accuracy isn't enough. Annex IV expects metrics broken down by relevant subgroups. Design your testing framework to capture disaggregated metrics from the start, not as an afterthought.

Static documentation

Documentation that was accurate at launch but hasn't been updated is non-compliant. Set up an update process triggered by system changes and maintain proper version control.

How Haffa.ai Helps

Our platform handles much of the Annex IV documentation work for you. Register your AI system once, and the document generator creates pre-filled Annex IV documentation from your system data. Every document is version-controlled with full change history, and each section maps directly to the relevant Articles and Annex IV requirements.

You can export in PDF, Word, and structured data formats for regulatory submission. When you update your AI system registry, the platform flags sections that may need revision.

Run a free risk assessment to see what documentation you need, or check out our plans for the full documentation generator.