/**
 * glossary.ts
 *
 * Centralized metric glossary used by Studio and dashboard alike. Each entry
 * is keyed by a canonical slug. The value carries:
 *
 *   - `label`: the canonical user-facing label, e.g., "Overall Score". Used
 *     by the dashboard's `<MetricLabel>` and may be reused as table-header
 *     copy where space allows.
 *   - `long`: full description for tooltips and help drawers. Copy was
 *     ported verbatim from the Studio-era `packages/studio/src/glossary.ts`.
 *
 * A `short` field is a planned future addition for tight contexts where the
 * `long` description is too verbose. Adding it later is a non-breaking
 * schema change.
 *
 * @see docs/design-docs/scenario-matrix/evaluation-ceiling.md (three reference points)
 * @see docs/architecture.md (scoring model)
 */
export interface GlossaryEntry {
    label: string;
    long: string;
}
export declare const GLOSSARY: {
    readonly overallScore: {
        readonly label: "Overall Score";
        readonly long: "A weighted average across all feature areas, using the gold scoring profile: Task Completion (50%), Code Correctness (25%), and Doc Coverage (25%).";
    };
    readonly docLift: {
        readonly label: "Doc Lift";
        readonly long: "How much the docs help, compared to the model's training data alone. Calculated as ceiling minus floor, where ceiling includes Doc Coverage and floor does not. Higher is better.";
    };
    readonly actualScore: {
        readonly label: "Actual Score";
        readonly long: "How well an AI agent scores when it has to find docs on its own through web search and page fetching. This is the real-world scenario. Only available in full mode.";
    };
    readonly retrievalGap: {
        readonly label: "Retrieval Gap";
        readonly long: "The score lost because agents can't find or use all the relevant docs. Calculated as ceiling minus actual. Lower is better; zero means agents find everything.";
    };
    readonly infraEfficiency: {
        readonly label: "Infra Efficiency";
        readonly long: "What percentage of the docs' potential quality actually reaches agents (actual ÷ ceiling). 100% means agents find and use all relevant docs perfectly.";
    };
    readonly floor: {
        readonly label: "Floor";
        readonly long: "Output-quality composite without documentation — Task Completion (60%) and Code Correctness (40%) only. Doc Coverage is excluded because it's undefined when no docs are provided. This tells you what the model already knows from its training data.";
    };
    readonly ceiling: {
        readonly label: "Ceiling";
        readonly long: "Score with gold-standard docs injected directly into the prompt. This is the best the documentation can do.";
    };
    readonly actual: {
        readonly label: "Actual";
        readonly long: "Score when an AI agent finds docs on its own through web search and page fetching. This is the real-world experience.";
    };
    readonly retGap: {
        readonly label: "Ret. Gap";
        readonly long: "Quality lost to discoverability (ceiling minus actual). The gap between what the docs could deliver and what agents actually get.";
    };
    readonly efficiency: {
        readonly label: "Efficiency";
        readonly long: "What fraction of the docs' quality reaches agents in practice (actual ÷ ceiling, shown as a percentage).";
    };
    readonly invertedRetGap: {
        readonly label: "Inverted Retrieval Gap";
        readonly long: "⚠️ Inverted retrieval gap: agents that can't find the docs actually score higher, because the docs hurt performance. This usually means there's a doc quality problem.";
    };
    readonly score: {
        readonly label: "Score";
        readonly long: "Ceiling composite for this feature area: Task Completion × 50% + Code Correctness × 25% + Doc Coverage × 25%.";
    };
    readonly taskCompletion: {
        readonly label: "Task Completion";
        readonly long: "Can the LLM implement the requested feature? Graded 0–100.";
    };
    readonly codeCorrectness: {
        readonly label: "Code Correctness";
        readonly long: "Is the generated code idiomatic, correct, and following best practices? Graded 0–100.";
    };
    readonly docCoverage: {
        readonly label: "Doc Coverage";
        readonly long: "Did the docs provide the information needed to implement the feature? Graded 0–100. This dimension only contributes to the ceiling composite (with docs) — it's excluded from the floor composite because it's undefined without documentation.";
    };
    readonly tests: {
        readonly label: "Tests";
        readonly long: "Number of test cases in this feature area.";
    };
    readonly overallDelta: {
        readonly label: "Overall Δ";
        readonly long: "Change in overall score between the two runs. Positive means the experiment scored higher.";
    };
    readonly actualDelta: {
        readonly label: "Actual Δ";
        readonly long: "Change in actual (agent-retrieved) score between runs. Positive means agents did better.";
    };
    readonly retGapDelta: {
        readonly label: "Ret. Gap Δ";
        readonly long: "Change in retrieval gap between runs. Negative is good here: it means the gap shrank and agents found more relevant docs.";
    };
    readonly efficiencyDelta: {
        readonly label: "Efficiency Δ";
        readonly long: "Change in infrastructure efficiency between runs. Positive means agents are capturing more of the docs' potential.";
    };
    readonly baseline: {
        readonly label: "Baseline";
        readonly long: "The reference run you're comparing against.";
    };
    readonly experiment: {
        readonly label: "Experiment";
        readonly long: "The new run you're evaluating.";
    };
    readonly delta: {
        readonly label: "Delta";
        readonly long: "Difference between experiment and baseline. Positive means improvement, negative means regression.";
    };
    readonly change: {
        readonly label: "Change";
        readonly long: "Whether the change is meaningful: improved, regressed, or unchanged (within the noise threshold).";
    };
    readonly lowScoringJudgments: {
        readonly label: "Low-Scoring Judgments";
        readonly long: "The grading model's explanations for tests that scored below 70/100.";
    };
    readonly judgmentReason: {
        readonly label: "Judgment Reason";
        readonly long: "The grading model's natural language explanation of what went wrong.";
    };
    readonly healthStrong: {
        readonly label: "Strong (80+)";
        readonly long: "Feature areas scoring 80 or above. The docs are working well for these features — AI agents produce correct, complete implementations.";
    };
    readonly healthAttention: {
        readonly label: "Needs Attention (70–79)";
        readonly long: "Feature areas scoring 70–79. These are okay but could be improved — there may be gaps in specific dimensions like doc coverage or code correctness.";
    };
    readonly healthWeak: {
        readonly label: "Weak (<70)";
        readonly long: "Feature areas scoring below 70. The docs are not providing enough support for AI agents to implement these features correctly.";
    };
    readonly negativeDocLiftMetric: {
        readonly label: "Negative Doc Lift";
        readonly long: "Number of areas where the documentation actually hurts AI performance — the model scores higher without docs than with them. This usually means the docs contain outdated patterns or incorrect examples.";
    };
    readonly weakAreas: {
        readonly label: "Weak Areas";
        readonly long: "Feature areas where the overall score is below 70. These need the most attention — low scores mean AI agents consistently struggle to implement these features.";
    };
    readonly docsHurt: {
        readonly label: "Docs Hurt Performance";
        readonly long: "Areas where the floor score (no docs) is higher than the ceiling score (with docs). The documentation may be actively misleading the model. These docs should be reviewed.";
    };
    readonly retrievalIssues: {
        readonly label: "Retrieval Issues";
        readonly long: "Areas where AI agents can find less than 70% of the available doc quality. The docs exist and are good, but agents can't discover them through search. Consider improving page titles, metadata, or search engine indexing.";
    };
    readonly dimWeaknesses: {
        readonly label: "Dimension Weaknesses";
        readonly long: "Individual grading dimensions scoring below 50 within an area. These are the specific skills where AI agents fail most — task completion (can it build the feature?), code correctness (is the code right?), or doc coverage (did it use the docs?).";
    };
    readonly efficiencyAnomalies: {
        readonly label: "Efficiency Anomalies";
        readonly long: "Areas where agent efficiency exceeds 100% — meaning agents perform better with self-found docs than with gold-standard docs injected directly. This can indicate doc quality issues (injected docs confuse the model) or agent memorization.";
    };
    readonly docLiftWins: {
        readonly label: "Doc Lift Wins";
        readonly long: "Areas where documentation boosts AI performance by 5 or more points. Higher doc lift means the docs are providing crucial information that the model doesn't already know.";
    };
    readonly retrievalExcellence: {
        readonly label: "Retrieval Excellence";
        readonly long: "Areas where AI agents successfully find and use at least 85% of the available doc quality through web search. Good retrieval means your docs are well-indexed and easy for agents to discover.";
    };
    readonly modelBreakdown: {
        readonly label: "Model Breakdown";
        readonly long: "Break down scores by individual LLM model. The default 'All Models' view shows the cross-model average. Select a specific model to see how it performed independently — useful for spotting models that struggle with specific feature areas.";
    };
    readonly strengths: {
        readonly label: "Strengths";
        readonly long: "What's working well: high-scoring areas, dimensions where the docs are strong, and areas where AI agents successfully find and use the documentation.";
    };
    readonly recommendations: {
        readonly label: "Recommendations";
        readonly long: "Prioritized remediation plan from gap analysis. Each recommendation identifies a documentation problem, the affected feature area, and the estimated score lift from fixing it.";
    };
    readonly totalPotentialLift: {
        readonly label: "Total Potential Lift";
        readonly long: "Aggregate potential score lift if all identified gaps were fixed. This is a conservative estimate — each gap targets the median of non-bottlenecked dimensions, not 100.";
    };
    readonly failureMode: {
        readonly label: "Failure Mode";
        readonly long: "The type of failure the grader emitted. Cross-cutting modes apply to any dimension (api-error, model-limitation, false-floor, unclassified). Per-dimension extensions cover documentation problems (missing-docs, incorrect-docs, outdated-docs, poor-structure), spec adherence (spec-mismatch), tool use (tool-misuse, chaotic-process, missing-recovery), and knowledge probes (factual-error, incompleteness, currency-violation, hallucination).";
    };
    readonly estimatedLift: {
        readonly label: "Estimated Lift";
        readonly long: "Estimated composite score improvement if this gap is fully fixed. Based on raising bottleneck dimensions to the median of non-bottlenecked dimensions.";
    };
    readonly confidence: {
        readonly label: "Confidence";
        readonly long: "How confident we are in this diagnosis (D0049 ceiling-cross-check derivation). High = the grader's emitted failure mode agrees with the structural ceiling-decomposition signal. Medium = signals disagree (or the ceiling pattern is not informative for this score). Low = passing scores never classify; treat as absent.";
    };
    readonly agentBehaviorOverview: {
        readonly label: "Agent Behavior";
        readonly long: "How AI agents interacted with your documentation during evaluation: what they searched for, which pages they visited, and how much time they spent on network requests.";
    };
    readonly searchQueries: {
        readonly label: "Search Queries";
        readonly long: "The exact search queries agents used to find documentation. Helps you understand how agents discover your content and whether your docs appear for relevant queries.";
    };
    readonly docSlugsVisited: {
        readonly label: "Unique Doc Slugs";
        readonly long: "Documentation page slugs that agents actually visited during evaluation. Compare against canonical docs to see if agents found the right pages.";
    };
    readonly externalDomains: {
        readonly label: "External Domains";
        readonly long: "Non-Sanity domains that agents contacted during evaluation. High external domain counts may indicate agents couldn't find what they needed in your docs.";
    };
    readonly avgDocPagesVisited: {
        readonly label: "Avg Pages Visited";
        readonly long: "Average number of documentation pages visited per test. Higher counts can mean agents need to consult many pages (complex task) or can't find the right one quickly.";
    };
    readonly avgSearchesPerformed: {
        readonly label: "Avg Searches";
        readonly long: "Average number of web searches performed per test. High search counts can indicate docs are hard to discover through search engines.";
    };
    readonly avgNetworkTimeMs: {
        readonly label: "Avg Network Time";
        readonly long: "Average time spent on network requests per test. Includes page fetches, search queries, and API calls.";
    };
    readonly totalRequests: {
        readonly label: "Total Requests";
        readonly long: "Total number of HTTP requests the agent made during the test, including searches, page visits, and API calls.";
    };
    readonly totalBytesDownloaded: {
        readonly label: "Total Bytes Downloaded";
        readonly long: "Total bytes downloaded by the agent. Large downloads may indicate the agent is fetching many pages or very large documents.";
    };
    readonly dimTaskCompletion: {
        readonly label: "Task Completion Δ";
        readonly long: "Change in task completion between runs. Positive means implementations are more complete.";
    };
    readonly dimCodeCorrectness: {
        readonly label: "Code Correctness Δ";
        readonly long: "Change in code correctness between runs. Positive means better code quality.";
    };
    readonly dimDocCoverage: {
        readonly label: "Doc Coverage Δ";
        readonly long: "Change in doc coverage between runs. Positive means the docs are providing more useful information.";
    };
    readonly areaDelta: {
        readonly label: "Area Δ";
        readonly long: "Score change for this area compared to the previous evaluation run.";
    };
    readonly sourceProduction: {
        readonly label: "Production";
        readonly long: "Production source — docs fetched from the live production dataset. Scores reflect what real users and AI agents experience today.";
    };
    readonly sourceBranch: {
        readonly label: "Branch";
        readonly long: "Branch source — docs fetched from a branch or draft dataset. Use this to preview how content changes affect scores before publishing.";
    };
    readonly sourceLocal: {
        readonly label: "Local";
        readonly long: "Local source — docs fetched from local files or a local dev server. Useful for testing doc changes before pushing.";
    };
    readonly reportScore: {
        readonly label: "Score";
        readonly long: "The overall ceiling composite for this evaluation run: Task Completion (50%), Code Correctness (25%), and Doc Coverage (25%), averaged across all feature areas.";
    };
    readonly reportMode: {
        readonly label: "Mode";
        readonly long: "The evaluation mode determines which reference points are measured. Different modes test different aspects of how AI agents interact with documentation.";
    };
    readonly reportTrigger: {
        readonly label: "Trigger";
        readonly long: "What initiated this evaluation run. Knowing the trigger helps you understand whether a score change was from a content edit, a code deploy, or a scheduled check.";
    };
    readonly modeBaseline: {
        readonly label: "Baseline";
        readonly long: "Baseline mode — tests the model with gold-standard docs injected directly. Measures ceiling performance (best the docs can do).";
    };
    readonly modeFull: {
        readonly label: "Full";
        readonly long: "Full mode — runs baseline + agentic. Compares ceiling (injected docs) against actual (agent-retrieved docs) to measure retrieval gap and infrastructure efficiency.";
    };
    readonly modeAgentic: {
        readonly label: "Agentic";
        readonly long: "Agentic mode — the AI agent finds docs on its own via web search. Measures real-world performance: can agents actually discover and use your documentation?";
    };
    readonly modeObserved: {
        readonly label: "Observed";
        readonly long: "Observed mode — records how agents interact with docs without scoring. Captures search queries, pages visited, and browsing patterns for analysis.";
    };
    readonly modeDebug: {
        readonly label: "Debug";
        readonly long: "Debug mode — a diagnostic run for pipeline development. May use non-standard configurations or limited task sets.";
    };
    readonly triggerManual: {
        readonly label: "Manual";
        readonly long: "Manually triggered — someone ran the evaluation pipeline by hand, either locally or via the Studio UI.";
    };
    readonly triggerCi: {
        readonly label: "CI";
        readonly long: "CI-triggered — the evaluation ran automatically as part of a pull request or merge pipeline.";
    };
    readonly triggerSchedule: {
        readonly label: "Scheduled";
        readonly long: "Scheduled — the evaluation ran on a recurring schedule (e.g. nightly or weekly) to track score trends over time.";
    };
    readonly triggerWebhook: {
        readonly label: "Webhook";
        readonly long: "Webhook-triggered — a content change in Sanity triggered the evaluation automatically. Helps catch doc regressions early.";
    };
    readonly triggerCrossRepo: {
        readonly label: "Cross-Repo";
        readonly long: "Cross-repo — triggered from another repository via the dispatch API. Used when external repos want to validate their docs against AILF tasks.";
    };
};
export type GlossarySlug = keyof typeof GLOSSARY;
