AI Platforms & Models

RAG Development

When answers must reflect your own current material rather than what a model learned during training, and every answer needs to be traceable to a source.

Retrieval-augmented generation grounds responses in documents fetched at query time, which is what allows citation and keeps answers current without retraining anything.

In an enterprise setting the hard part is not retrieval quality but permissions. Retrieval must respect who is allowed to see what, enforced at the retrieval layer, or the system becomes an efficient way to reach documents a user could not otherwise open.

What the work includes

  • Ingestion, chunking, and indexing tuned to how your material is actually structured
  • Permission-aware retrieval evaluated per request, not per interface
  • Citation of sources so answers can be verified independently
  • Evaluation of retrieval quality separately from generation quality
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When people cannot find material because they do not know the exact words the author used, which is most of the time.

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When the answer to most internal questions already exists somewhere, and the cost is finding it rather than producing it.

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// Common Questions

Questions
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Common questions about RAG Development.

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The model answers from material fetched from your systems at request time rather than from what it learned during training. That is what allows an answer to reflect your current documents, and to cite where each claim came from.

Usually because chunking ignores how the source material is actually structured — splitting mid-table, separating a heading from the text it governs, or cutting a clause from its context. Retrieval quality is mostly determined before any model is involved.

By evaluating them separately. If the correct passage was never retrieved, no amount of prompt work will fix the answer. Measuring them together is why teams spend weeks tuning prompts for what is fundamentally a retrieval problem.

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