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This calculator treats context as critical and supporting. It uses weights so must-have items influence the score more than optional items.
TokenUtilization = tokens_used / token_budget
TokenFitness = 0.4 + 0.6·(1 − |TokenUtilization − Target| / Target)
RedundancyFactor = 1 − min(0.5, 0.5·Redundancy)
OverallScore = 100 · WeightedCoverage · TokenFitness · RedundancyFactor
If required items are zero, coverage defaults to 100% for that group.
- List the critical items your model must know to answer correctly.
- List supporting items that improve accuracy, style, and edge cases.
- Count how many of each are currently included in your prompt.
- Estimate token budget and tokens used, then set a target utilization.
- Enter an honest redundancy estimate if your prompt repeats content.
- Click Calculate Coverage and review suggestions above the form.
- Download CSV or PDF to compare prompt versions over time.
A typical scenario for a medium-complexity prompt.
| Critical req | Critical inc | Supporting req | Supporting inc | Budget | Used | Redundancy | Weighted coverage | Overall score |
|---|---|---|---|---|---|---|---|---|
| 10 | 9 | 12 | 8 | 3000 | 1900 | 12% | ~82.61% | ~74–84%* |
*Overall score varies with target utilization and weights.
Coverage as a Quality Signal
Context coverage summarizes whether a prompt contains the information needed to complete a task reliably. In audits, teams often track a critical coverage target of 90% and a supporting coverage target of 70%. When both groups are high, reviewers see fewer “missing requirement” failures, especially on edge cases and policy constraints. This calculator reports weighted coverage so gaps in must‑have items are more visible than gaps in nice‑to‑have details. Across iterative runs, a 10‑point coverage gain often reduces follow‑up prompts, lowering latency and cost for batch evaluations in real deployments.
Weighting Critical and Supporting Context
The model uses weights (default wC=2.0, wS=1.0) to reflect asymmetric risk. For example, missing 1 of 10 critical items reduces the numerator by 2 points, while missing 1 of 12 supporting items reduces it by 1 point. If you change wC to 3.0 for safety‑sensitive prompts, the same miss causes a larger score drop, helping teams prioritize remediation work.
Token Fitness and Budget Pressure
Coverage alone is not enough; prompts can be “complete” but wasteful. Token utilization is tokens_used ÷ token_budget, and a target utilization (often 0.70) rewards compact prompts that stay below budget. Using 1900 tokens of a 3000 token budget yields 0.63 utilization, which is close to target and typically increases TokenFitness. If utilization exceeds 1.00, fitness falls sharply because truncation risk grows.
Redundancy and Prompt Maintenance
Redundancy estimates how much content repeats without adding new information. The calculator applies a penalty capped at 50%, so excessive repetition cannot dominate the score. A redundancy value of 0.12 (12%) produces a modest reduction, but values above 0.40 often signal that instructions, examples, or constraints are duplicated across sections. Removing repeated disclaimers and merging overlapping bullet lists usually improves both clarity and utilization.
Interpreting Scores for Iteration
Use the overall score to compare prompt versions, not to judge absolute “goodness.” Many teams treat 85–100 as production‑ready, 70–85 as acceptable with known risks, and below 70 as needing revision. The suggestions panel highlights whether to add missing critical items, rebalance supporting details, tighten token usage, or reduce redundancy. Exported CSV and PDF reports support peer review, change logs, and regression checks.
A critical item is a requirement the model must see to answer correctly, such as constraints, definitions, inputs, or evaluation rules. If missing, the response can become invalid or unsafe even when everything else is present.
Skim your prompt and mark repeated instructions, duplicated examples, and restated constraints. Divide repeated content by total content to get a rough percentage. Start with 10–20% for most prompts, then refine after edits.
Overall score multiplies coverage by token fitness and a redundancy factor. A prompt can be complete yet inefficient, exceed budget, or contain heavy repetition, which lowers fitness and the final score.
For most workflows, 0.65–0.75 balances completeness and headroom. Use lower targets for long outputs or tool calls. Use higher targets only when budget is tight and truncation risk is acceptable.
Yes. Run each version with the same counting approach, then export CSV or PDF. Comparing weighted coverage, utilization, and redundancy side by side makes improvements and regressions easy to document.
Not necessarily. A perfect score may indicate overfitting to a checklist or overly strict targets. Aim for stable, repeatable scores with strong critical coverage, reasonable utilization headroom, and low redundancy.