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Yugensys engineers RAG applications that ground answers in your enterprise knowledge - retrieval, evaluation and citations engineered in, with retrieval quality you can measure.
RAG grounds a model's answers in your own knowledge instead of its training data: a question retrieves the most relevant passages from governed enterprise sources, retrieved context is ranked and assembled, the model answers from that context, and every answer carries citations back to its sources, with retrieval and answer quality measured continuously.
RAG earns its place when the answers your business needs already live in its own knowledge - documents, systems and records - and guesswork is not acceptable: knowledge assistants, enterprise search and grounded AI applications.
Where answers don't depend on your knowledge - or the knowledge isn't governed well enough to ground them - RAG adds machinery without adding truth. We'll say so.
Answers grounded in your knowledge, with retrieval quality you can measure.
Your knowledge made searchable by meaning - embedded, indexed and retrievable at answer time.
Finding the right passages, not just matching words - retrieval engineered and measured against real questions.
The best evidence first: retrieved candidates ranked so the model answers from what matters most.
Every answer traceable to its sources - grounded in retrieved context and cited, never guessed.
Capability map: every capability as an engineered system.
A RAG application is a pipeline: retrieval, ranking, context and generation engineered as one measured system, with citations and evaluation closing the loop.
Select a stage. This is the conceptual pipeline - how a question becomes a grounded, cited answer.
Objective
The real user question - the pipeline's contract.
Objective
Find the most relevant passages from governed sources.
Objective
Order candidates by relevance.
Objective
Assemble what the model is allowed to answer from.
Objective
Generate from retrieved context, not training-data guesswork.
Objective
Grounded and relevant to the question asked.
Objective
Cite sources; measure retrieval and generation quality continuously with evaluation sets that reflect real usage.
The RAG pipeline
Are the right passages found and ranked? Retrieval quality is measured continuously against evaluation sets that reflect real usage, not one-off benchmarks.
Is the answer grounded in what was retrieved, relevant and correctly cited? Groundedness is evaluated as a first-class property, not assumed.
Data leakage and sensitive-data exposure are addressed as part of the engineering process.
On both halves of the system: retrieval quality (are the right passages found and ranked?) and generation quality (is the answer grounded in what was retrieved, relevant and correctly cited?) - measured continuously with evaluation sets that reflect real usage, not one-off benchmarks.
Talk to the engineers who would build it - about your sources, your questions and how grounded the answers must be.