Stress-test prompts across temperatures and contexts for repeatable, dependable results. Score stability with weighted signals. Improve reliability, reduce drift, and ship confident outputs daily.
| Scenario | Runs | Consistent | Similarity | Adherence | Variance | Hallucination | Latency CV |
|---|---|---|---|---|---|---|---|
| Baseline prompt, temp sweep | 20 | 14 | 0.82 | 88% | 22 | 6% | 15 |
| Added format schema and refusals | 20 | 17 | 0.90 | 94% | 12 | 3% | 14 |
| Tool-augmented prompt with retrieval | 30 | 25 | 0.86 | 91% | 18 | 4% | 22 |
The calculator combines six signals into a single stability score from 0 to 100. Higher scores indicate more consistent behavior across runs and settings.
| Signal | Definition | Mapped Score |
|---|---|---|
| Consistency Rate | Consistent outputs ÷ test runs × 100 | 0–100 |
| Similarity | Average semantic similarity × 100 | 0–100 |
| Adherence | Instruction adherence percent | 0–100 |
| Variance Stability | 100 − variance index | 0–100 |
| Hallucination Safety | 100 − hallucination rate | 0–100 |
| Latency Predictability | 100 − latency CV | 0–100 |
Final Score = wc·Consistency + ws·Similarity + wa·Adherence + wv·(100−Variance) + wh·(100−Hallucination) + wl·(100−Latency), where weights are normalized to sum to 1.
A prompt is stable when the same intent produces comparable outputs across runs, temperatures, and everyday input variation. This calculator turns that idea into measurable signals: consistency rate, semantic similarity, adherence, variance, hallucination exposure, and latency predictability. Use the score as a control metric in your evaluation pipeline, not as a one-time badge. Track it alongside acceptance tests and incident metrics for releases.
Stability starts with a fixed suite of test cases covering typical, edge, and adversarial inputs. Run a temperature sweep, include tool and retrieval paths when applicable, and log outputs with hashes plus structured rubric labels. Consistent outputs should meet formatting rules, preserve key facts, and match required actions. Similarity can be estimated with embeddings, graders, or human pairing checks. Keep prompts, model settings, and test data versioned for auditability.
Variance index captures how far outputs wander in structure or content, even when they appear plausible. High variance often comes from ambiguous instructions, loose schemas, or unbounded “creative” latitude. Reduce variance by defining sections, acceptable vocabularies, and deterministic slot filling. If variance improves but similarity drops, your prompt may be over-constrained and losing intent fidelity. Rebalance by restoring flexible phrasing only where user value increases.
Hallucination rate should be tracked per domain, because risk rises when prompts request citations, numbers, or policies. Strengthen grounding with explicit “unknown” handling, citation requirements, and retrieval-first steps. Maintain a labeled set of known-false traps and measure the failure rate after every prompt edit. A stable prompt is not only consistent, it is consistently correct. Tie remediation to clear playbooks: add constraints, add sources, then retest.
Different products value different failure modes. Customer support may weight adherence and hallucination safety, while creative drafting may weight similarity and consistency. Normalize weights, record versions, and set promotion thresholds (for example, score above 80 with hallucination safety above 95). After deployment, re-run the suite on model updates, tool changes, and data shifts to prevent silent regressions. Use trend charts and alerts so declines are caught before customers report issues.
A higher score suggests outputs are more repeatable, instructions are followed more often, and risk signals like hallucination and drift are lower across your tested conditions.
Use at least 20 runs for quick checks, and 50+ for release gates. Include temperature sweeps, common user inputs, and edge cases so the score reflects real production usage.
Compute embedding similarity between outputs, use a grader model, or apply human pairwise review. Keep your method consistent over time so changes in the score reflect prompt changes, not measurement noise.
Score variance from 0 to 100 based on structural drift, missing sections, inconsistent facts, or shifting conclusions. Calibrate the scale using examples, then reuse the rubric for every evaluation cycle.
Yes, when your risk priorities differ. Safety-critical workflows can emphasize adherence and hallucination safety, while creative tasks can emphasize consistency and similarity. Always document the weights used for comparisons.
Re-test after prompt edits, model updates, tool changes, or data shifts. Many teams run nightly suites and alert on drops, so stability regressions are detected before users notice.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.