Retrieval Evaluation: What to Measure Before Launch
Separate retrieval failures from answer failures.
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Articles from Sprinklenet on AI governance, AI strategy, enterprise knowledge management, and government contracting.
Separate retrieval failures from answer failures.
Ask the supplier to demonstrate one allowed request, one denied request, a revoked permission, and a failed connector.
Start with a dependency record for one workflow: the source system, permitted users, required action, failure owner, and acceptance test.
Better procurement questions make vendor responses more comparable and reduce delivery risk.
Fine-tuning teaches a model how to behave, not what is true.
A chat transcript is not an audit trail.
Most contracting firms cannot say which AI tools are approved to touch contract data, and that gap matters more than model access.
A fluent chatbot demo hides most of the work that decides whether the system survives real users.
Embeddings measure how alike two pieces of text are; a graph records that two things are related, how, and on whose authority.
Every document your retrieval system indexes is a potential instruction channel into the model.
Sprinklenet has opened version 1.0 of the Knowledge Spaces developer APIs for teams building governed, multi-tenant AI workflows with controlled retrieval, configuration, and usage…
Most federal AI pilots fail at transition, not at the demo, because no program office has agreed to own the result.
Most weeks I send one short read on enterprise AI: what’s working in real deployments, what’s not, and what I’d do in your seat.
Jamie Thompson, Founder and CEO