[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"post:data-engineering-for-llms-rag-pipelines":3},{"id":4,"slug":5,"title":6,"excerpt":7,"body":8,"tags":9,"read_minutes":14,"status":15,"published_at":16,"updated_at":17},"2c6ce459-fe9d-4d84-97d3-3c489e051dd5","data-engineering-for-llms-rag-pipelines","Data Engineering for LLMs: Building Production RAG Pipelines","Every organization is bolting AI onto its products; few have infrastructure to feed enterprise data into models reliably. Parsing, chunking, metadata, and vector databases — the new ETL.","\u003Cp>For twenty years, data engineering meant moving structured rows between systems. LLMs changed the raw material: the knowledge an enterprise wants its AI to use lives in PDFs, wikis, contracts, and support tickets — and retrieval-augmented generation (RAG) is how that knowledge reaches the model without retraining it.\u003C\u002Fp>\u003Cp>The pattern is simple to whiteboard: parse documents, split them into chunks, embed the chunks, store the vectors, retrieve the relevant ones at question time. The gap between that whiteboard and a production system is almost entirely a data engineering problem.\u003C\u002Fp>\n\u003Ch2>Parsing is where quality is won or lost\u003C\u002Fh2>\u003Cp>Garbage in applies with brutal force here: a mangled table in a parsed PDF becomes a confident wrong answer at query time. Real documents have multi-column layouts, tables, headers that carry meaning, and scanned pages needing OCR. Layout-aware parsers that preserve structure — table extraction to markdown, heading hierarchies kept intact — routinely beat naive text extraction by a wide margin.\u003C\u002Fp>\u003Cp>Treat parsing as a first-class pipeline stage with its own tests: golden documents whose expected output is checked on every run, exactly as you would test a dbt model.\u003C\u002Fp>\n\u003Ch2>Chunking and metadata: the underrated levers\u003C\u002Fh2>\u003Cp>Chunks must be small enough to embed precisely but large enough to carry context. Fixed-size splitting is a baseline, not a strategy — structure-aware chunking that respects section boundaries, with modest overlap, performs better on almost every corpus. When a chunk loses its context ('the policy above does not apply to contractors' — which policy?), prepend the document title and section heading to the chunk before embedding.\u003C\u002Fp>\u003Cp>Metadata is what turns similarity search into reliable retrieval. Extracting document type, date, department, and access level alongside each chunk enables filtered queries — 'search only current HR policies' — and, critically, lets you enforce document-level permissions at retrieval time rather than hoping the model keeps secrets. A vector store without an access-control story is a data leak waiting for a clever prompt.\u003C\u002Fp>\n\u003Ch2>It's a pipeline, not a script\u003C\u002Fh2>\u003Cp>The demo is a notebook; the product is an idempotent, monitored pipeline. Documents change, so you need change detection and re-embedding without full rebuilds. Embedding models version, so store the model name alongside every vector — mixing embeddings from different models silently corrupts retrieval. Vector databases like Pinecone or Milvus handle the serving layer, but freshness, dedup, and lineage remain your job.\u003C\u002Fp>\u003Cp>Measure retrieval like the production system it is: a golden set of questions with known source passages, recall checked on every deploy. Teams that skip this discover their AI feature degraded weeks after the fact — from a user complaint.\u003C\u002Fp>",[10,11,12,13],"AI","RAG","LLM","Data Engineering",8,"published","2026-07-01T00:00:00Z","2026-08-27T16:17:41.412599Z"]