RAG systems and knowledge workflows
Retrieval-augmented generation is an architecture, not a default. Decide whether your product needs it — and what it takes to make it trustworthy if it does.
Start here when knowledge lives in documents, tickets or transcripts, and answers need grounding, citations and boundaries.
When this fits
- The knowledge is locked in documents, tickets, transcripts or internal systems.
- Answer quality depends on provenance, freshness, and who is allowed to see what.
- A chatbot is tempting, but the real problem might be retrieval, workflow or review.
What an engagement looks like
- A RAG fit check: corpus quality, retrieval strategy, permissions, evaluation.
- Chunking, indexing, citation and freshness design that can be measured instead of argued about.
- A human review loop for answers that touch customers, money or compliance.
What you leave with
- A clear yes-or-no on whether RAG is worth building now.
- Retrieval quality as a measured number instead of a standing debate.
- A system that says it doesn't know when it doesn't — instead of improvising.
Case studies behind this
- Round-trip translation of structured documents without breaking them
Translated files open like human-edited originals: same structure, same formatting, and no silent reflow or document corruption.
- A multi-provider AI gateway with built-in cost accounting
Model choice became configuration, new providers inherited common controls, and spend became visible while requests were still running.
LLM systems
For teams past the prompt stage, now facing service boundaries, fallback paths, observability, cost control and evaluation.
Open topicRAG fit check
Retrieval-augmented generation solves a specific problem. Three checks that tell you whether you have that problem — before you build the pipeline.
Read guideCommon AI project failure modes
AI projects rarely die at the demo. They die months later, in production, for reasons that were visible on day one.
Read guide