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Why Do We Need to Measure LLM Health?
The rise of AI Large Language Models (LLMs) like GPT-4 and its successors has changed the way organizations think, plan, and automate. But how do you know your AI is healthy, delivering value reliably, fairly, and efficiently? That’s where an LLM Health Index comes in. At Savvy CFO, we believe every AI investment should be measured with as much scrutiny as a financial one.
Introducing the LLM Health Index
Think of the LLM Health Index as a balanced scorecard for your AI. It captures the key dimensions that matter to organizations: Reliability, Accuracy, Bias, Efficiency, Explainability, and Security.
Here's our framework for building a robust LLM Health Index:
1. Reliability (0–100)
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Definition: Does the model deliver consistent results over time and under different conditions?
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Indicators:
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Uptime % (API/server)
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Frequency of outages/errors
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Consistency of answers across queries
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2. Accuracy (0–100)
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Definition: How closely do the LLM’s responses match verifiable truths or expected outputs?
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Indicators:
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Human evaluation (spot checks)
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Automated accuracy tests (benchmarks)
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Client/user satisfaction surveys
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3. Bias (0–100; lower = better)
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Definition: To what extent does the LLM produce responses that are free of unwanted bias (gender, race, etc.)?
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Indicators:
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Frequency of flagged bias in outputs
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Results of bias testing suites
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Diversity of training/test data
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4. Efficiency (0–100)
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Definition: How well does the LLM use resources to deliver value?
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Indicators:
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Latency (response speed)
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Cost per query
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Compute resource utilization
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5. Explainability (0–100)
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Definition: How easy is it to understand why the LLM made a certain decision or produced a response?
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Indicators:
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Presence of explainability tools/features
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Clarity of logs and outputs
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User ability to request/receive explanations
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6. Security (0–100)
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Definition: How robust is the LLM against data leaks, prompt injections, or misuse?
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Indicators:
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Results of penetration tests
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Incident response records
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Data privacy measures in place
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How to Calculate Your LLM Health Index
Assign weights to each dimension based on your business priorities (for example: Reliability 25%, Accuracy 25%, Bias 15%, Efficiency 15%, Explainability 10%, Security 10%).
For each dimension, collect scores (0–100) using your preferred methods. Calculate the weighted average for an overall Health Index out of 100.
Example Method:
Break the 0–100 range into meaningful performance bands. For example:
| Score Range | Meaning | Example (for Accuracy) |
|---|---|---|
| 90–100 | Excellent | Responses are 95%+ accurate on benchmark tests |
| 70–89 | Good | Mostly accurate, with occasional errors |
| 50–69 | Moderate/Acceptable | Correct in many cases but has gaps |
| 30–49 | Weak | Often incorrect or inconsistent |
| 0–29 | Poor/Unacceptable | Fails most quality or reliability checks |
Example Calculation:
| Dimension | Score | Weight | Weighted Score |
|---|---|---|---|
| Reliability | 90 | 0.25 | 22.5 |
| Accuracy | 85 | 0.25 | 21.25 |
| Bias | 80 | 0.15 | 12 |
| Efficiency | 70 | 0.15 | 10.5 |
| Explainability | 60 | 0.10 | 6 |
| Security | 75 | 0.10 | 7.5 |
| Total | 79.75 |
Your LLM Health Index is 79.8/100.
Takeaways
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Track over time: Health should be monitored regularly, just like financial KPIs.
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Set improvement goals: Use the Index to drive quarterly targets (e.g., “raise Accuracy by 10%”).
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Report to stakeholders: Make LLM health as visible as any other key metric.
Final Thought
AI is a powerful business tool, but only when managed thoughtfully. The Savvy LLM Health Index helps organizations maximize trust, ROI, and peace of mind.
