More reliable Gen AI in HVAC/R: CAREL’s new white paper on context engineering
More reliable Gen AI in HVAC/R: CAREL’s new white paper on context engineering
From prompt engineering to context engineering: a practical approach to making generative artificial intelligence more useful, verifiable and responsible in technical and managerial work
CAREL has published its new white paper, “Gen AI in HVAC/R: making it more reliable. From prompt engineering to context engineering”, dedicated to the use of generative artificial intelligence in the HVAC/R sector and to the conditions needed to turn it into an effective tool supporting analysis, decisions and work quality.
Written by Giandomenico Lombello, Senior Advisor and member of the Group’s AI Committee, the document starts from a clear premise: there is no “perfect prompt” capable, on its own, of making Gen AI reliable. In a field where decisions may have technical, operational, economic and safety implications, output quality depends above all on the quality of the context provided to the model.
This is where the shift from prompt engineering to context engineering begins: not only asking better questions, but building the information conditions that allow the model to produce useful, verifiable responses that are consistent with the real case. The document highlights how the risk is not limited to hallucinations, but also includes plausible answers that are formally correct yet insufficiently anchored to the physical, plant or operating conditions.
The white paper interprets Gen AI as Augmented Intelligence: a tool that does not replace expertise, judgement and experience, but amplifies them. It also recalls security, data and compliance as essential prerequisites when handling service reports, plant logs, customer specifications, drawings, tender documents or technical documentation.
Through ten operational applications, the white paper shows how to use Gen AI in a more structured way: from the Zoom Out / Zoom In method to multimodality, from AI Projects to grounding and RAG, through to role-based review and Deep Search.
