Questions That Expose a Weak AI Proposal


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Posted by ML_Systems_mr on September 01, 2026 at 17:50:45:

In Reply to: Hello from a beginner forum member posted by glacier on June 12, 2026 at 10:06:30:

Vendor selection often fails when the proposal hides the boundary between product engineering and model experimentation. A useful scope states which data sources are available, what the fallback path does and how incorrect outputs are reviewed. See AI engineering scope questions for the service frame. Technical diligence should also cover deployment access and exit terms. AI software company evaluation provides another reference point, while https://ai-software-development.net can be shared as the plain project URL. Reject a plan that depends on production data nobody can access or on an external API with no substitution path. A smaller, testable first release is easier to assess than a broad promise covering every workflow.



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