Organisations are rushing to put generative AI in front of staff and customers, and most quickly discover that a general-purpose model cannot answer questions about internal procedures, product terms or regulatory guidance. Retrieval-augmented generation has become the standard pattern for connecting models to trusted content, but moving from a demonstration to a dependable service demands engineering discipline and clear governance.

The Enterprise Retrieval-Augmented Generation (RAG) Architecture and Governance Training Course spans two weeks and treats RAG as a complete system. The first week covers foundations and construction: language model behaviour and limits, content ingestion and cleaning, chunking and metadata, embeddings, vector stores, retrieval methods and answer generation with citations. The second week is about making that system dependable at enterprise scale. Topics include hybrid retrieval and reranking, query rewriting, agentic and multi-step patterns, evaluation of retrieval and answer quality, observability, latency and cost optimisation, document-level permissions, privacy and data residency, red-teaming and compliance with emerging AI governance frameworks.

Every day includes coding or design labs. Participants progress from a simple pipeline to a secured, evaluated assistant over a realistic document set and present an architecture decision record and governance pack at the close.

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

• Explain how large language models work, where they fail and why grounding is needed.
• Build ingestion pipelines that parse, clean, chunk and enrich enterprise documents.
• Select embedding models and vector stores and tune similarity search.
• Implement hybrid retrieval, reranking and query transformation to raise answer quality.
• Construct prompts and responses that cite sources and handle missing evidence.
• Evaluate retrieval and generation using test sets, metrics and human review.
• Enforce document-level access control, privacy protections and defences against prompt injection.
• Establish monitoring, cost management and governance processes for production RAG services.

The course is intended for technical and governance professionals who design or oversee enterprise AI assistants, including:

• Software engineers and backend developers
• Data engineers and data scientists
• Machine learning and AI engineers
• Solution, cloud and enterprise architects
• Technical product owners and AI programme managers
• Information security and data protection officers
• Knowledge management and digital transformation leads
• Risk, audit and compliance staff reviewing AI systems

Participants complete the course with:

• A working RAG prototype over a document collection, with source citations.
• A reference architecture and architecture decision record for their organisation.
• An evaluation set and scoring routine for retrieval and answer quality.
• A security design covering permissions, prompt injection and data handling.
• A cost and latency model for production operation.
• A governance pack with risk register, approval checkpoints and monitoring plan.

The course is laboratory-driven, with concepts introduced only when the next build step requires them:

• Daily coding labs in Python using open-source libraries and a shared document corpus
• Architecture whiteboard sessions comparing design options and trade-offs
• Evaluation clinics where groups test and critique each other's systems
• Security red-team exercises against participant prototypes
• A capstone assistant presented with an architecture decision record

On completion of the ten days, participants earn a CPD-accredited Certificate of Completion from Vision Reach Global Consultancy.

The award is based on attendance, completion of the daily labs and delivery of the capstone presentation.