Spring GDS 25th Anniversary
A logistics company that ships to 190 countries built something to ship to itself.
RAG (Retrieval-Augmented Generation)
What is RAG?
Retrieval-augmented generation is a pattern that feeds a language model relevant source material before it answers. Instead of relying only on what the model memorized during training, the system first retrieves passages from your own knowledge base, then asks the model to answer using that retrieved context.
The retrieval step is what makes it work. A user question gets converted into a query against a document store, often a vector database that matches on meaning rather than keywords. The most relevant chunks come back, get stitched into the prompt, and the model generates its answer grounded in that material. A help desk assistant that quotes your actual policy docs, with citations, rather than guessing from general training, is RAG in practice.
This solves two real problems. Models trained months ago do not know your latest product or last week's pricing, and they hallucinate when asked about things outside their training. RAG keeps the answer tied to current, owned, verifiable sources, which is the difference between a demo and something you can put in front of customers.
RAG at Dallonses
When a client wants a chatbot that answers from their own content, RAG is usually the honest way to get there. We index the knowledge base, tune how documents get chunked and retrieved, and measure whether the right context actually shows up before we worry about the model's wording. Bad retrieval makes even a great model look stupid, so that is where the work starts.
Our AI and machine learning solutions wire retrieval, generation, and citations into one loop, with evaluation on top so accuracy is something you can track instead of hope for. We have built chatbots and virtual assistants that stay grounded in a company's real documentation, and we are blunt about where RAG fits and where it does not. The goal is answers people trust, not a clever demo that falls apart on the second question.
Want an assistant that answers from your own knowledge, not the open internet? Let's build it.
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