Not from a course. This stack runs in production right now, and the open-source side has closed the gap enough that self-hosting is a real default choice today, not a budget compromise.
This is the working pattern behind the NewsRx production line in Story 3 and Story 5.
Turn PDFs, docx, and HTML into clean text. Apache Tika or Unstructured.io.
Split text into retrievable pieces. Hierarchical for structured docs (3 to 5x better F1), semantic for narrative text. LangChain / LlamaIndex splitters.
Convert each chunk to a vector. BGE-M3 (dense, sparse, and multi-vector in one model) or Qwen3-Embedding-8B (#1 open MTEB multilingual, 32K context).
Run the embedding model at scale. TEI for auto-batched production serving, or raw vLLM/SGLang DIY.
Index vectors for search. Qdrant or Milvus (43K+ stars, hybrid dense+sparse). Fuse with BM25 via Reciprocal Rank Fusion, then rerank with BGE-reranker-v2.
Feed the top reranked chunks to the LLM. Self-hosted: vLLM serving an open model (Qwen3-32B and similar).
Grounding lives in a knowledge graph beside Postgres running hybrid RAG and vector search. A GenAI platform in the Cortex class doesn't just draft text. It does data pulls, and the human in the loop rarely catches a bad one, because reviewers aren't taught critical thinking and a plausible number gets accepted. The bad data flows into analysis. Business, shipping, and manufacturing paths change on it. The changed outputs get written back, and the bad data is reintroduced to the very system it came from. The loop closes, and the estate starts poisoning itself.
Ground retrieval in ontology and semantics so a pull carries its meaning and lineage with it. That's the knowledge graph's job beside the vector index, and it's why GraphRAG matters below. Treat "the reviewer will catch it" as an unverified gate, the same gate that reported green in 1984. Instrument the write-back path, because that's the point of irreversible consequence — the 17 instrumented write-path call sites at NewsRx are exactly this, applied to a publishing estate.
Benchmarks (BEIR, MTEB, Anthropic's own contextual retrieval work) settled it. BM25 plus dense embeddings, fused with reciprocal rank fusion, beats either alone, and a cross-encoder reranker adds another 5 to 15 points of MRR on hard queries.
12 months ago dense-only vector search was the default pattern most tutorials taught. Now hybrid search runs in 72% of production RAG systems.
The biggest paradigm shift of the year. Instead of a fixed retrieve-then-generate pipeline, the model now controls retrieval itself, deciding to ask for more evidence, rewrite its own query, or stop early (Self-RAG, FLARE patterns).
12 months ago retrieval was a preprocessing step. Now it is an action the agent takes mid-reasoning — which is exactly why ungoverned data pulls became the failure mode above.
Microsoft's GraphRAG (MIT-licensed) moved knowledge-graph-augmented retrieval from research papers into real, adopted production architecture.
12 months ago GraphRAG was a research pattern. Now it is a named, shipping option for teams that need entity and relationship reasoning — ontology and semantics — not just similarity search.
BGE-M3 and Qwen3-Embedding-8B now rival or beat proprietary embedding APIs on MTEB, trained on massive multilingual corpora, both freely self-hostable.
12 months ago closed APIs were the safe default for embedding quality. Now a self-hosted open-weight model is a legitimate first choice.
Amgen's first GxP-validated GenAI system for FDA NDA submissions (ICH-standard CTD content, RAG-grounded, feeding Structured Content Authoring into Veeva Vault) took a 16-person build. Six Bain consultants, three data scientists, three business analysts, three medical specialist application writers, and John owning the ICH application process. Vault had no native agentic authoring then, so the RAG-to-SCA loop was architected and governed from scratch under GxP change control.
The build was custom because the platform hadn't caught up yet.
Veeva's own Agentic Authoring application integrates natively with Vault RIM and Word, proactively drafts submissible documents, and monitors incoming data to initiate drafting. Clinical, Regulatory, and Medical AI Agents are slated for this same month, and Veeva's roadmap calls RIM+AI the standard for 2026 to 2027.
What took a 16-person custom build in 2023 is a configurable vendor feature by 2026.
Built today, the Amgen system likely wouldn't need a bespoke GenAI-to-SCA pipeline at all. It would configure and extend the vendor's native agents. That's not a knock on the original build. The industry converging on the same architecture two years later is validation the design was right. What it shows is that work genuinely custom-necessary one year becomes vendor commodity a couple of years later, and knowing when to stop building and start configuring is the strategist judgment call, not a technical one.
"I ran plasma flow calculations on a Cray-1A for DARPA in 1980. 160 megaflops, state of the art at the time. Apple's A17 Pro does over 5 teraflops, about 31,000 times faster, in something that fits in a pocket. That's the same curve as a bespoke 2023 GenAI pipeline against a 2026 native platform feature, just compressed into two years instead of forty-five."