Workshops - RAG for AEC Data: Ground AI in Your Project Documents
Ground a language model in your own documents to support real AEC tasks across design, specification and compliance. Build a simple but accurate RAG from scratch over the UK Building Regulations, with attention to what makes it trustworthy.
What you'll learn
In this workshop you'll build a Retrieval-Augmented Generation (RAG) system from scratch in C# — chunking and embedding real construction datasets, storing the vectors, and grounding a local LLM's answers in what it retrieves. Along the way you'll see first-hand why grounding matters, climbing the ladder from a blind chatbot, to a naive keyword grep, to semantic search that recalls the right records even when they never use your exact words.
What you'll cover
- How retrieval-augmented generation works
- Ingesting, parsing and chunking documents
- Embeddings and a vector store
- Grounded answers with source citations
- Evaluating quality and mitigating hallucination
Who it's for
Technically comfortable AEC professionals; no ML experience needed.
How the day runs
- 1
Chunk the data
Break the defect register and building-regs PDF into retrievable units, each carrying a citable ID.
- 2
Generate embeddings
Turn every chunk into a vector with a local embedding model, so meaning becomes coordinates a machine can compare.
- 3
Store the vectors
Build a vector store from scratch, then swap in an off-the-shelf one behind the same interface.
- 4
Keyword vs. semantic search
Query naive keyword matching alongside cosine-similarity search to expose where literal matching fails.
- 5
See it cluster
Project the embeddings into the Rhino viewport, colouring points by metadata so related chunks group by meaning.
- 6
Ground the answers
Feed the retrieved chunks to the LLM and compare a blind chatbot, a keyword baseline, and full RAG on the same question.
A look inside
1 day · Live online · Max 14 places
£479 + VAT
per person
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