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RAG engineering
Retrieval-augmented systems for document Q&A and for internal operations, including a Gemini legal assistant and n8n workflows. Written from shipped work by Muhammad Huzaifa Shahbaz, AI Systems Engineer at Digital Dividend Global.
What I build
- Document assistants that answer from a private corpus and return the passage they used.
- Operational RAG wired into n8n so repetitive internal lookups do not depend on someone pasting context into a chat window.
Architecture
- The legal assistant uses Gemini for generation, LangChain to orchestrate retrieval, and MongoDB for documents and embeddings.
- Queries hit vector search first. The model answers from retrieved passages instead of from the prompt alone.
- Ops automation pairs the same retrieval idea with n8n. At Digital Dividend that workflow is credited with about 8 hours saved per week.
Production constraints
- Legal answers without a source are not usable. The assistant is built to retrieve case material and contracts, not to freestyle.
- Published results on the legal assistant: 95%+ accuracy on classification and question answering in their evaluation, and about a 70% reduction in document review time versus manual research.
- Token cost and stale embeddings are the ongoing bills. Retrieval has to be scoped to the documents that matter for the question.
Stack
Gemini · LangChain · MongoDB · Next.js · n8n · RAG
Tradeoffs
- A vector store over a full-document prompt is cheaper and easier to cite, and it fails when the chunking splits the clause that actually answers the question.
- n8n is the right tool for an ops workflow a small team can edit. It is the wrong place for a latency-sensitive user-facing agent.
Projects
- AI Legal Assistant — Gemini RAG assistant for legal document search and cited answers.
External verification
Questions
What RAG systems has he shipped?
A Gemini and LangChain legal assistant with MongoDB vector search, plus operational RAG workflows automated with n8n at Digital Dividend Global.
How is the legal assistant grounded?
It retrieves from stored legal documents and embeddings before answering, and the public write-up describes citation-backed answers rather than ungrounded chat.