RAG Development
RAG is how a model answers from your documents instead of its training data — with citations you can check and a pipeline you can maintain.
Retrieval-augmented generation is the difference between a model that guesses and a system that knows: your documents, policies, product data or codebase, indexed and retrieved at question time so the model answers from evidence it can cite. Done well, it turns an unusable pile of internal PDFs into a queryable knowledge base your team and customers actually trust.
The engineering is unglamorous and decisive: how content is chunked, how embeddings are built and refreshed, how search combines keyword and vector matching, how context fits the model window, and how the system says "not in the sources" instead of improvising. Each of those choices is measurable, which is why every build ships with an evaluation set — real questions, known-good answers, scored retrieval — before anyone relies on it.
This practice builds RAG systems for support knowledge, internal documentation, product catalogues and research collections — on your infrastructure or a managed stack, sized honestly to your volume. The deliverable includes the refresh pipeline: when your documents change, the index follows, because a stale knowledge base is worse than none.
What the engagement covers
- Document pipeline: ingestion, chunking and embedding tuned to your content type
- Hybrid retrieval — keyword and vector search combined, because each fails where the other wins
- Citation-grounded answers with source references the reader can verify
- Refresh pipeline — new and updated documents flow into the index on a schedule
- Evaluation set: your real questions, scored retrieval quality, reported before launch
- Access controls matching your permissions — a knowledge base that respects who may see what
- Query interface: standalone page, embedded widget, or API for your own product
Honest limits
What this is deliberately not.
Not this: Answering questions your documents do not contain — the system is built to say so, not to improvise
Not this: Reasoning over live databases with no document layer — that is a different architecture, scoped differently
Not this: Set-and-forget deployments — content drifts, so the refresh pipeline and review cadence are mandatory parts
Not this: Replacing structured search where structured search wins — faceted catalogues sometimes just need facets
Questions · RAG systems
Asked before building.
Because fine-tuning teaches style and patterns, not facts — it is the wrong tool for knowledge that changes, and it costs more to keep current. RAG retrieves the live document at question time, cites it, and updates when the document does. Fine-tuning earns its place for tone and format consistency; for knowledge, retrieval wins on cost, accuracy and auditability. The fine-tuning page covers when the trade flips.
Measured, not promised: the evaluation set scores retrieval on your real questions, and that number is reported before launch. Accuracy depends on your content — clean, current sources answer well; contradictory stale documents answer badly, and the audit says so up front. Systems that cannot cite a source are built to refuse, which is a feature.
Yes, and where it matters it shapes the architecture: retrieval filtered by the asker's permissions, so internal documents never surface in public answers. Permission-aware RAG costs more to build than a flat index and is non-negotiable where mixed content lives — the scope reflects that honestly.
Scheduled re-indexing as documents change, monitoring for retrieval quality drift, and periodic evaluation re-runs. That is why the refresh pipeline ships with the build rather than as an afterthought — a knowledge system is a living thing, and the runbook hands your team the watering can.
Related: all AI services · automation services · the vetting guide.
Also in this section
AI Chatbot Development · AI Agent Development · LLM Integration Services · LLM Fine-Tuning · AI Consulting & Audit · AI Search Visibility
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