Generative AI / Knowledge Engineering
From Search to Synthesis: Designing RAG Pipelines
Wall-street banks and aerospace teams alike are wiring vector search into large-language models so staff get answers rooted in policy, not guesswork. Morgan Stanley’s GPT-4 assistant scans 100k research notes and returns citations in seconds; NASA’s mission engineers do the same with flight-software manuals. Behind both wins sits a retrieval-augmented-generation (RAG) stack that filters, scores and stitches text chunks before the model speaks.
The Retrieval-to-Generation Blueprint
Why banks and space labs abandoned end-to-end fine-tuning
Morgan Stanley’s advisers once trawled 15 years of PDF research; now they ask a GPT-4 chat box and get a citation-linked summary in under a second. The firm credits “hard-gated retrieval” with slashing hallucinations and winning compliance sign-off. openai.com NASA’s Jet Propulsion Laboratory echoes the logic: mission ops chatbots query a vector index first and inject only the top passages into the prompt, keeping flight-rules intact. github.com
Anatomy of a modern RAG stack
StageDesign goalKey toolsIngest & chunkSplit docs into 300–500 token spans with metadata (security label, owner).LangChain, LlamaIndex, Azure AI Search indexersEmbed & storeTurn chunks into dense vectors, store in pgvector, Pinecone or Astra DB.OpenAI text-embedding-3-small, Cohere, Sentence-T5RetrieveHybrid BM25 + vector search to balance keyword fidelity and semantic reach.Azure AI Search hybrid, Elasticsearch ES-8, VespaRerankFilter & score for freshness, authority; compress to 1–2 k tokens.Cohere rerank-2, bge-reranker-baseGeneratePopulate a template prompt, stream answer, attach citations.GPT-4o, Anthropic Claude 3, Mistral-Large
Microsoft’s RAG design guide emphasises groundedness, completeness and relevancy as the three evaluation pillars; their template pipeline above aligns with that rubric. learn.microsoft.com
Case notes from finance and aviation
- Morgan Stanley Assistant – 200 advisers in private beta, < 3 % hallucination rate after hybrid search + strict cite-checking. businessinsider.com
- Bank of America “four-layer” AI – retrieval sits one layer below GPT, gating every query to avoid compliance breaches. businessinsider.com
- NASA VECTOR project – open-sourced embeddings pipeline handles 20 GB of mission docs with GPU-light rerank phase. github.com
Reference architecture (edge-to-cloud)
- DocOps – nightly crawler pushes SharePoint, Confluence and S3 docs into a Delta Lake.
- Chunk & Embed – Spark UDF batches 2 000 docs/min; embeddings land in pgvector.
- Hybrid retriever – BM25 top-50 ⭢ vector top-20 ⭢ rerank to final 8 chunks.
- Prompt composer – inserts chunks, role, style guide, and a JSON schema guard.
- Observability – langfuse logs token usage, latency, groundedness scores.
Azure’s architecture centre calls this “search-augment-rerank-generate” and provides evaluation notebooks out-of-the-box. learn.microsoft.com
Build-and-prove pilots (4–8 weeks)
PilotEffortOutcomeP-1 Doc-to-chat MVP – index 10 GB of PDFs, serve via GPT-4-turbo2 wksDemo retrieval accuracy ≥ 90 %P-2 Rerank swap-test – compare bge-base vs. Cohere-rerank1 wkChoose model that trims hallucinations mostP-3 Eval harness – automate groundedness & latency KPIs1 wkDashboards for CISO & QAP-4 Guardrails – JSON schema + profanity filter1 wkProves safe, structured outputP-5 Roll-out kit – deploy Helm chart + Terraform for prod2 wksCloud-agnostic hand-off to DevOps
References
- OpenAI. Morgan Stanley uses GPT-4 to deepen client relationships (Apr 2025). openai.com
- Microsoft Learn. Design & develop a RAG solution (AI Architecture Center, Feb 2025). learn.microsoft.com
- Microsoft Tech Community. Optimising retrieval for RAG apps: vector + hybrid (Oct 2024). techcommunity.microsoft.com
- Microsoft Cloud Blog. Common RAG techniques explained (Feb 2025). microsoft.com
- Business Insider. Morgan Stanley and BoA focus AI on employee assistants (May 2025). businessinsider.com
- NASA JPL. VECTOR: Retrieval-augmented chat for mission ops (GitHub, 2024). github.com
- OpenAI Cookbook. RAG evaluation templates (GitHub, 2025). github.com
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