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RAG for Engineering

End-to-end design and implementation of Retrieval-Augmented Generation systems built for the complexity of real engineering environments.

The problem

Generic RAG tutorials work on clean, simple datasets. Engineering documentation doesn't look like that. It's dense, inconsistent, domain-specific, and distributed across formats that standard pipelines mishandle.

Most teams underestimate the gap between "getting RAG to work in a demo" and "building a RAG system that engineers actually trust for critical decisions."

That gap is where I work.

Who this is for

  • Engineering organizations that have tried to implement RAG internally and hit walls
  • Technical teams that need a RAG system built right — architecture, retrieval quality, and production stability
  • Companies evaluating RAG as infrastructure before committing to a full rollout
  • Projects where retrieval precision matters more than retrieval speed

This service is more technical in scope than Technical Document AI. It's for teams that want to understand what they're getting, not just that it works.

What I build

A production-grade RAG system designed around your specific corpus, query patterns, and reliability requirements.

Architecture decisions I make explicit:

  • Chunking strategy (fixed-size, semantic, document-structure-aware)
  • Embedding model selection based on your domain vocabulary
  • Vector database configuration (Weaviate, Pinecone, or alternatives)
  • Retrieval method: dense, sparse, or hybrid
  • Reranking layer if precision requirements demand it
  • Context window management for long-document queries
  • Evaluation framework to measure retrieval quality before and after tuning

What you get at the end:

  • A working system with documented architecture decisions
  • An evaluation suite you can run after future changes
  • Clear handoff documentation for your technical team
  • Recommendations for future improvements and scaling

My approach

  1. Corpus analysis — Understand your document types, sizes, languages, and formats. Identify structural patterns and edge cases.
  2. Query pattern definition — Work with your domain experts to define the 20–30 most important query types the system needs to handle well.
  3. Architecture design — Select the right combination of chunking, embedding, retrieval, and generation for your specific requirements.
  4. Build and tune — Implement, evaluate against real queries, iterate on retrieval quality.
  5. Adversarial testing — Test for failure modes: hallucination, missed context, contradictory documents, edge cases.
  6. Deployment and handoff — Deploy to your infrastructure, document everything, train your team.

Use cases

  • Building a queryable knowledge base from a library of engineering standards and specifications
  • Indexing decades of project documentation for historical query access
  • Creating a compliance assistant that retrieves relevant regulatory text for a given situation
  • Powering internal tooling that surfaces relevant procedures during active operations
  • Building the retrieval layer for a larger AI system that requires grounded, accurate context

Experience signals

  • Built a complete RAG system for Quiet Links (scientific research corpus, 200+ papers, 6-week delivery)
  • 22 years of domain experience in engineering environments where retrieval accuracy has operational consequences
  • PhD in Engineering — I understand the technical documentation I'm building systems around
  • Technical stack: Python, LangChain, Weaviate, Pinecone, OpenAI, Anthropic, FastAPI, PostgreSQL

See the Quiet Links case study →

Frequently asked questions

What's the difference between this and Technical Document AI?

Technical Document AI is scoped around getting your team a working assistant. RAG for Engineering is for teams that need to understand the architecture, evaluate retrieval quality, and own the system technically. The deliverables include the evaluation framework and architectural documentation, not just the working system.

Can you evaluate an existing RAG system instead of building one from scratch?

Yes. If you have a RAG system that isn't performing well, I can audit the architecture, identify the failure modes, and recommend or implement improvements.

Which vector databases do you work with?

Primarily Weaviate and Pinecone. I've also worked with Chroma and pgvector. Database selection depends on your infrastructure, scale, and query requirements — I'll recommend based on your specific case.

How do you measure retrieval quality?

I use a combination of hit rate, Mean Reciprocal Rank (MRR), and context precision/recall evaluated against a held-out set of ground-truth query-answer pairs built from your domain. I define these metrics with you during scoping so there's no ambiguity about what "good" means.

What if my documents keep changing?

We design the ingestion pipeline with update handling in mind. Depending on your change frequency, this means scheduled re-ingestion, delta updates, or event-driven indexing. This is part of the architecture discussion during scoping.

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