North American clients, delivered by the OpsBreak team
Retrieval-augmented generation pipelines over client documents
Language model answers grounded in the client's own documents
Context
The OpsBreak team has built retrieval-augmented generation (RAG) pipelines for clients who wanted a language model to answer questions from their own material.
Client details are withheld for confidentiality, so this page describes the work in general terms.
The challenge
A general language model does not know a company's internal documents, and it can give confident answers that are wrong.
A useful system has to find the right passages first, then answer from them in a way people can check.
What we did
- 1
Prepare the documents
Documents are cleaned, split into passages and turned into embeddings so they can be searched by meaning.
- 2
Build retrieval
A search index returns the most relevant passages for each question, combining keyword and vector search where it helps.
- 3
Generate grounded answers
The model answers using the retrieved passages and points back to its sources.
- 4
Test the answers
A set of test questions checks retrieval and answer quality, so changes can be measured instead of guessed.
Results
- Language model answers grounded in the client's own documents
- Answers that point back to their sources
- A test set for checking quality after each change
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