Retrieval-Augmented Generation (RAG) Systems under the EU AI Act

Overview

This course addresses operational compliance for Retrieval-Augmented Generation (RAG) systems under Regulation (EU) 2024/1689 (the EU AI Act). A RAG system generates output by retrieving material from an external knowledge source and incorporating that material into the response.

Whether a RAG system is high-risk depends on intended purpose, not on the architecture. Where that purpose places the system in a high-risk class, the Act continues after the system is put into service. Providers and deployers then carry duties on risk management, data and data governance, logging, human oversight, accuracy and robustness, quality management, use in operation, and post-market monitoring.

The course translates the articles named for this programme into purpose-specific controls for a live RAG pipeline: what is retrieved, from where, how it is ranked, how it is used in the generated output, and how that chain is watched after go-live.

Why RAG is treated as its own operational object

A RAG system has two moving parts the Act will see as one system in use: the retriever and the generator. Errors can sit in either part, or in the join. A biased, stale or incomplete corpus produces a different failure from a model that invents a citation. Source drift - the corpus changing after the system was tested - is a production fact, not a design footnote.

Those facts change how the listed articles are applied. Data governance has to reach the corpus and the retrieval index, not only the training set. Oversight has to see retrieved passages and generated text. Logs have to reconstruct the query, the hits, the sources used and the output. Monitoring has to cover retrieval quality, hallucination and the propagation of bias from sources into answers.

Articles in scope

The course is built around the articles already named for this programme.

• Article 8 - compliance with the Chapter III, Section 2 requirements, read through intended purpose, the generally acknowledged state of the art, and the risk-management system.
• Article 9 - the risk-management system across the life cycle, including risks that appear only when retrieval and generation run together.
• Article 10 - data and data governance relevant to the corpus, the index and any datasets used to build or tune the pipeline.
• Article 12 - automatic recording of events to a level that supports traceability of retrieval and output.
• Article 14 - human oversight of retrieved and generated content, including when a person must be able to intervene.
• Article 15 - appropriate accuracy, robustness and cybersecurity for the running pipeline.
• Article 17 - a quality management system covering design, testing, data, change and post-market processes.
• Article 26 - deployer obligations, including use according to instructions and monitoring of operation.
• Article 72 - the provider's post-market monitoring system, including performance in use and serious-incident reporting where it applies.

The course does not treat every RAG deployment as high-risk. It does not treat high-risk status as optional once intended purpose meets the Act's tests.

What you will learn

By the end of the course, participants will be able to:

• classify a RAG system under the EU AI Act and identify which high-risk obligations follow from its intended purpose;
• apply Article 8's three interpretive lenses - intended purpose, state of the art, and risk management - to a live RAG environment;
• design human oversight for retrieved and generated content, including when hallucination or a bad source must escalate;
• set out proportionate post-market monitoring covering retrieval quality, source drift, hallucination and bias carried through from sources;
• specify runtime observability, audit trails and incident response for the RAG pipeline in use, not for a generic generator.

Course structure

Six modules. Each module stays inside the articles listed above.

1. Regulatory context and the Article 8 lenses

How the Act classifies systems by intended purpose. When a RAG use is high-risk and when it is not. How intended purpose, state of the art and the risk-management system are read together so compliance is attached to the live pipeline.

2. Human oversight under Article 14

What a person must be able to see: the query, the retrieved material, the sources, and the generated answer. When oversight must stop or correct an output. Escalation when retrieval is empty, conflicting or clearly wrong, and when the generator departs from the retrieved material.

3. Logging, traceability and observability

Article 12 as it applies to a retrieval-plus-generation sequence. Reconstructing which sources were hit and which were used. Article 10 as it applies to the corpus and the index. Article 15 as it applies to accuracy and robustness of the joined system.

4. Post-market monitoring under Article 72

Collecting and analysing data on how the RAG system performs in use. Retrieval quality over time. Source drift after the last test. Hallucination rates that matter for the intended purpose. Bias that moves from the corpus into answers. The deployer's related duty under Article 26 to use the system as instructed and to watch its operation.

5. Operational risk controls and incident response

Article 9 after deployment: risks that appear only when the corpus, the index and the generator are connected. Containing a bad source. Stopping a pipeline that is retrieving from an unintended store. Recording the incident so the risk-management file can be updated.

6. Integration and audit readiness

How Articles 17 and 8 require these operational controls to sit inside a quality-management system. What a notified body or market-surveillance authority would need in order to assess the live RAG system against the claimed purpose. The record means the technical and operational information the Act already requires. The course does not add a separate product pack.

Who the course is for

The course is written for people responsible for RAG systems that are in production, or that are being prepared for a high-risk intended purpose under the articles above. It assumes familiarity with the EU AI Act. It is not a survey of retrieval methods.