RAG Development Services
LLM answers grounded in your actual documents and data — not the model's general knowledge.
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RAG (retrieval-augmented generation) is the technique of retrieving relevant chunks of your own documents or data at query time and feeding them to an LLM as context, so its answer is grounded in your actual content instead of its general training. DuCodes builds RAG pipelines — document ingestion, chunking, embeddings, retrieval, and generation — for chatbots, internal search, and AI features that need to be accurate about your specific business.
A general-purpose LLM doesn't know your product catalog, your internal policies, or last week's support tickets. RAG solves that by retrieving the relevant pieces of your own data at query time and including them in what the model sees before it answers — so the answer is grounded in your actual content rather than the model's general training.
The quality of a RAG system depends far more on the retrieval step — how documents are chunked, embedded, and ranked — than on which LLM sits at the end of the pipeline. We spend most of our effort there: getting the right context in front of the model, tuned against real questions your users actually ask.
Why work with DuCodes on this
Retrieval quality first
Chunking strategy, embedding choice, and ranking logic get tuned against your real content and real user questions — not left at framework defaults.
Evaluated against real queries
We test retrieval accuracy against a representative set of real questions before considering the system done, not just a handful of demo queries.
Works with what you already have
PDFs, help center articles, database records, internal wikis — we build ingestion pipelines around your actual document sources, however messy they are.
Honest about what RAG can't fix
If your underlying documentation is out of date or contradictory, RAG will surface that clearly rather than fix it — we'll tell you when the real problem is content, not retrieval.
How we work
Content audit
What documents/data actually need to be retrievable, and how current and consistent they are.
Ingestion & chunking design
How documents get split and embedded so retrieval returns genuinely relevant context, not just keyword matches.
Retrieval tuning
Testing and adjusting retrieval against real questions before wiring in generation.
Generation & guardrails
Prompting the LLM to answer only from retrieved context, with a defined fallback when nothing relevant is found.
Evaluation & iteration
Ongoing measurement of answer accuracy against real usage, since document sets and questions change over time.
Technology we use
Frequently asked questions
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