RAG quality is decided before the model ever answers
Retrieval-augmented generation is simple to describe: fetch the right passages from your documents, hand them to an LLM, get an answer grounded in your actual content with citations back to the source. In practice, almost every disappointing RAG system fails at retrieval, not generation. If the right passage never reaches the context window, no prompt can save the answer. DEV3LOPCOM, LLC, an Austin, Texas consultancy, builds RAG systems with that priority order — retrieval first, generation second, evaluation over both. We also run RAG in our own products, Bee and Canopys, so the trade-offs below come from operating these systems, not reading about them.
The trade-offs we engineer deliberately
Chunking is the first one. Small chunks retrieve precisely but strip away context; large chunks preserve context but dilute embedding relevance and waste tokens. The right size depends on your documents — contracts, wiki pages, and support tickets all want different treatment, and often structure-aware splitting beats any fixed size.
Search strategy is the second. Dense embeddings catch paraphrase and meaning; keyword search catches exact part numbers, names, and codes that embeddings blur. Most production systems want hybrid retrieval with a reranking step that puts the best passages first, because context windows reward precision at the top.
Then grounding: prompts that require citations, that prefer “I don’t know” when the retrieved context does not contain the answer, and faithfulness checks that catch claims the sources do not support.
How an engagement runs
Assess: we inventory your document sources, formats, update cadence, and permission model, and define what a correct answer looks like. Design: chunking strategy, embedding and vector store selection, hybrid search and reranking, and access control enforced at retrieval time so users only see documents they are cleared for. Build: the ingestion pipeline, the retrieval service, and — before launch — an evaluation set of real questions scored for retrieval relevance and answer faithfulness. Handoff: your team gets the eval harness, ingestion runbooks, and a system they can extend.
Ingestion is where our data engineering roots pay off. Keeping a vector store synchronized with living document sources is pipeline work, the same discipline behind our ETL practice and engagements like Nielsen’s trillion-record pipelines.
Problems we fix
- Chatbots that answer confidently from stale or wrong passages because ingestion never re-indexes updates.
- Retrieval that fails on exact identifiers — SKUs, error codes, people — because the system is embeddings-only.
- RAG deployments with no eval set, where quality is judged by whoever complained last.
- Permission leaks where search surfaces documents a user could never open directly.
Why dev3lop
Senior, 100% onshore engineers who treat your document corpus like the data asset it is. If your roadmap runs past Q&A into systems that take action, our AI agent development practice builds on the same foundation, and LLM integration covers the model gateway underneath. Contact us with a description of your documents and the questions your team needs answered — we will scope a pilot with evaluation built in from day one.