Building RAG Pipelines That Enterprises Can Trust

Almost every enterprise AI request we get comes down to one question: can the model answer from our documents instead of from the internet? Retrieval-augmented generation (RAG) is the answer. The gap between a demo and a dependable system is mostly engineering discipline.

Chunk by meaning, not by character count

Fixed 1,000-character chunks split tables and clauses in half. We chunk on document structure, such as headings, list items and table rows, and attach the parent section title to every chunk. That one change usually improves retrieval more than switching embedding models.

Measure before you tune

• Build a golden set of 50–200 real questions with known answers.
• Track retrieval recall separately from answer quality.
• Re-run the set on every prompt or model change, the same way you run CI tests.

If you can't measure an answer's quality, you can't promise it to a customer.