Building NLP pipelines

[AI_Developer_S]

RAG systems often look straightforward in a prototype, but production quality depends on much more than model selection.

In our experience, the main variables are document preparation, retrieval quality, metadata filtering, access control, citation support and a repeatable evaluation set. Monitoring failed retrievals is also more useful than tracking only generic model metrics.

We build these systems at [url=https://ai-development-services.com/]AI Development Services[/url]. Which retrieval or evaluation metric has been most useful in your environment?

[astonmartin]

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[Author]

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