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Using Quantlix without an ML background
Quantlix is a control layer around AI products: safety rules, document lookup, execution timelines, and automated quality checks. You can be a strong application developer and still use it effectively—the product labels advanced terms like evals and retrieval beside plain English in the UI.
What “quality checks” (evals) are
Saved test questions and scoring that tell you if answers got worse after you changed a prompt, model, or document set—similar to unit tests for software, applied to AI outputs.
What “knowledge lookup” (retrieval) is
Searching your own uploaded or synced documents to ground answers, often with source references (citations). It is how “chat over my PDFs” works without training a custom model.
What templates are for
Pre-built outcomes—safer chat, document Q&A, approval workflows—so you start from something that already runs instead of wiring every step from scratch.
When Quantlix is worth it
Helpful when you have real users or production traffic, need visibility into what the model did, or must enforce safety and budgets consistently. For a one-off throwaway experiment with no risk, it may be early—that is normal; the same product is there when your app grows.