Generated text is not evidence
Language models can produce confident prose even with insufficient grounding. We explore systems that search defined collections, distinguish quotations from interpretations and expose the conditions behind the response.
Grounded retrieval
Connect a conversational interface to selectable reference collections where permissions and technical conditions allow.
Provenance by design
Show which documents were used and acknowledge when available material does not support a firm answer.
Testing and oversight
Add evaluation cases, update procedures, feedback mechanisms and safeguards aligned with the use case.
No promise of perfect accuracy
Even well-designed assistants can make errors. Responsible engineering requires testing, source checking, ambiguity handling and prominent limits on what the application can do.
Frequently asked questions
Are your AI answers guaranteed to be reliable?
No. The objective is traceable, inspectable sources and better controls, not a claim of infallibility.
What does RAG mean?
Retrieval-augmented generation combines language generation with searches across selected documents. Its results depend on data quality and system design.
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Documentation
Documentation principles for specialist nutrition AI: readable references, dates, uncertainty and practical source verification.
Learn more ↗Consultation
A technical consultation for nutrition AI, petfood software, documentary systems or web applications. Not a medical appointment.
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