The AI said users
could build a Ferrari.

The answers were making promises the product could not keep.

A product for a sister concern used vector-based retrieval-augmented generation to support its AI answers. During early use, the system selected the wrong references and hallucinated capabilities the product did not have.

One particularly revealing example was its claim that users could build a Ferrari using the app. That was an unsupported description of what the software could do.

A reference does not make an answer correct.

The problem was not simply whether the system returned a response. Its answers had to be useful in the context of the actual product. Wrong references and invented capabilities undermined that purpose.

The Ferrari example made the mismatch concrete: the AI could give a user a false understanding of what they would be able to achieve. The recovery needed to address answer quality and the boundaries of the product’s capabilities.

Tightening the system around the real product.

The founder tightened the system to address the incorrect references and unsupported claims. The answer-quality problems were resolved in approximately one week.

Why this belongs in the recovery record

This was AI/RAG work for a sister concern, carried out by the people behind RescueFoundry. The product is unnamed. It complements the client application rescues by showing experience with an AI system whose output itself needed correction.

For another RAG project, a representative question and a poor answer can be a useful starting point. The investigation then needs to establish why the failure occurs before selecting the changes.