Quick answer: a RAG (retrieval-augmented generation) pipeline connects a general-purpose LLM to your business's specific, current data at query time — it retrieves relevant passages from your documents and includes them in the prompt, so the model answers grounded in your actual information with a traceable source, rather than from training data alone. For a UK business, the architecture is the same as anywhere else; the main additional consideration is where the data actually lives.
What a RAG Pipeline Actually Consists Of
Three stages: ingestion (documents are chunked, embedded, and stored in a vector database — the chunking strategy is the single most impactful decision in the whole pipeline, covered in depth in our production RAG architecture guide), retrieval (a query is embedded and matched against stored chunks, ideally using hybrid search combining vector similarity with keyword matching), and generation (retrieved chunks are assembled into context and passed to the LLM with a prompt requiring citation back to the source chunk for every factual claim).
Choosing a Vector Database
pgvector is the right default for most UK businesses already running PostgreSQL — no new infrastructure dependency, and it handles corpora up to a few million chunks comfortably. Qdrant is worth considering when data residency is a hard requirement, since it can be self-hosted entirely inside your own infrastructure — relevant for a UK business that needs to keep data inside the UK or a specific region for UK GDPR reasons. We cover the full comparison, including Pinecone, in our vector database comparison piece.
UK GDPR and Data Residency
Where your embeddings and source documents are actually stored and processed matters under UK GDPR, particularly for a RAG system handling customer or employee personal data. A self-hosted vector database inside UK or EU infrastructure gives you full control over this; a fully managed, non-UK-hosted service means your data residency is bound by that vendor's regional offerings rather than your own infrastructure choices — worth confirming explicitly before committing to an architecture, not after.
Common Failure Modes to Design Around
Chunk-boundary hallucination (the answer spans two chunks and retrieval returns only one — fixed with better chunking or increased overlap), query-vocabulary mismatch (the user's phrasing doesn't match the document's phrasing — fixed with hybrid search or query expansion), and context-window overflow (too many retrieved chunks push the actual answer out of context — fixed by reranking and taking only the top results after reranking).
Evaluation, Not a One-Time Build
A RAG pipeline isn't something you configure once — it's an evaluation loop you run continuously as your document set and query patterns change. Shipping without an evaluation framework (a golden set of representative queries checked against expected answers, run on every deployment) means shipping blind to regressions.
If you're building a RAG system for your UK business and want architecture, evaluation, or data-residency guidance specific to your document set, reach out at info@digit.com.pk.