Mind the Gap: How AC Tech HVAC Helper Bridges LLM Weaknesses in Calculation and Reasoning

While Large Language Models (LLMs) like GPT-4 possess robust knowledge (recall) and strong comprehension (analysis), evaluation confirms that they still have critical limitations, particularly in the realm of practical application capability. Application tests the ability to utilize domain knowledge to solve tasks such as troubleshooting or equipment selection.

For the best-performing models, GPT-4 and GPT-3.5, three key limitations were identified:

The Three Gaps

1. Lack of Specific Domain Knowledge

GPT-4 sometimes demonstrated errors regarding specific, technical parameters. For instance, it made mistakes about the peak concentration values of propylene glycol—a detail that matters when working with glycol-based systems.

This limitation means that while the AI understands general principles, it may occasionally miss specific technical details that are critical for precise field work.

2. Poor Reasoning Capabilities

In certain instances, GPT-4 justified its options with reasons that were irrelevant to the selected answer, indicating flaws in the deductive process. This suggests that while the AI can identify correct answers, it doesn’t always reason through them correctly.

3. Critical Mistakes in Formula Application

Although LLMs correctly identify the necessary formulas and understand the underlying principles for calculation-based tasks (like calculating total pressure or head loss), they often make key errors in execution and application.

For example, GPT-4 made errors in applying the hydraulic power formula when calculating head loss, leading to an incorrect result that did not match the provided options. The AI understood the concept but failed in the execution—a critical gap for field technicians who need accurate calculations.

The AC Tech HVAC Helper Solution: Structured Application

The design of AC Tech HVAC Helper—providing a detailed step-by-step diagnosis followed by the cause and the solution—is the built-in mechanism to counteract these limitations:

Focus on Cognitive Strengths

The app prioritizes the LLM’s superior recall and analysis capabilities to accurately identify the cause of the problem and the necessary principle for the solution. By leveraging what AI does best, AC Tech HVAC Helper:

• Identifies root causes using extensive knowledge recall
• Analyzes system relationships to understand how components interact
• Provides diagnostic frameworks based on proven troubleshooting methodologies

Structured Output for Verification

By outputting a structured, step-by-step process, AC Tech HVAC Helper allows the professional user to easily audit and verify the logic. When calculations are required:

1. The AI provides the principles: Which formulas to use, what parameters matter, and why
2. You apply the calculations: Using your tools, measurements, or calculators
3. You verify the results: Trust but verify—the AI guides, you execute

This approach ensures the reliability of the final result and bridges the gap between theory and execution. It’s the difference between:

• ❌ AI does everything: Risk of calculation errors going unnoticed
• ✅ AI guides, you verify: Professional control with AI assistance

Real-World Example

Consider a scenario where you need to calculate head loss in a pumping system:

Traditional AI approach: AI calculates the result directly—if it makes an error, you might not catch it.

AC Tech HVAC Helper approach:

1. AI identifies: “You need to calculate head loss using the Darcy-Weisbach equation”
2. AI provides: The formula, the parameters needed (flow rate, pipe diameter, length, friction factor)
3. AI explains: How to find each parameter and why they matter
4. You calculate: Using your measurements and the provided framework
5. You verify: The result makes sense for your system

This structured approach transforms AI assistance from a black box into a transparent, verifiable process.

Why This Matters

This strategic design leverages the AI’s vast knowledge base while mitigating the risk of critical calculation errors, thereby ensuring the reliability required for HVAC professionals.

For field technicians, this means:

• Confidence: You understand the reasoning behind each step
• Control: You verify calculations using your measurements
• Reliability: Cognitive strengths are maximized, weaknesses are mitigated
• Learning: You gain deeper understanding through structured guidance

AC Tech HVAC Helper doesn’t hide its limitations—it acknowledges them and builds a framework that works around them, ensuring you get the best of AI assistance without the risk of blind trust.