AI Systems
Building Better RAG Features
Retrieval Quality Comes First
Most RAG problems are retrieval problems before they are model problems. If the right documents never reach the model, the answer will feel vague or wrong no matter how polished the prompt is.
Start by measuring whether your system retrieves the expected source for common user questions.
Chunk for the Reader and the Model
Chunking should preserve meaning. Splitting documents every fixed number of characters is easy, but it often cuts through a concept at the worst possible point.
Better chunks usually follow natural structure:
- Headings and subheadings.
- Short sections with a single idea.
- Tables or code examples kept intact.
- Metadata that preserves source, date, and ownership.
Show the Source
Users trust AI answers more when they can inspect where the answer came from. Source links, citations, and quoted snippets make the system easier to verify.
A grounded AI feature should help users move faster without asking them to trust blindly.
That is the difference between a demo and a product feature.