import type { EvalVerdict } from './evaluate.js';
import type { LlmProvider, TokenCounter } from './types.js';
/**
 * Which few-shot examples earn their tokens, measured rather than guessed.
 *
 * The `redundant-examples` advisory answers a *textual* question: does this
 * example look like an earlier one? That catches the way few-shot blocks actually
 * grow — copy the last one, change two fields — and it is the cheapest useful
 * thing to say, because it costs nothing.
 *
 * This answers a different and much stronger question: **does removing this
 * example change any answer?** Two examples can be textually unalike and teach the
 * same thing, and a block nobody has measured is usually where the tokens are: a
 * few-shot section is routinely most of a prompt.
 *
 * The method is leave-one-out against the prompt's own noise floor. Ask the full
 * prompt twice to find out how much the model disagrees with *itself*, then remove
 * one example and ask again. If the answer moves no further than the model already
 * moves on its own, that example is not doing observable work.
 *
 * **It spends the caller's money and must never run by default.** The bill is
 * `2 + examples` calls per input, and `plannedCalls` exists so a caller can print
 * the figure before deciding rather than discovering it afterwards.
 *
 * **What it cannot tell you.** An example may exist for a case these inputs do not
 * contain — the boundary condition somebody hit in production last March and added
 * a demonstration for. Removing it would change nothing measurable here and break
 * that case. So this reports "no effect on these inputs", never "delete this", and
 * the wording is deliberate: the strength of the claim is bounded by the inputs
 * given, and only the caller knows whether those cover what matters.
 */
export interface ExampleContribution {
    /** Position in the prompt's example block, from zero. */
    index: number;
    /** The example itself, so a report can quote its first line. */
    text: string;
    tokens: number;
    /**
     * Mean agreement between the full prompt's answer and the answer with this
     * example removed, across every input.
     */
    agreementWithout: number;
    /**
     * `indistinguishable` and `within-noise` both mean the removal changed nothing
     * this measurement can see. `diverges` means it did. `inconclusive` means the
     * model disagreed with itself too much for any of this to mean anything.
     */
    verdict: EvalVerdict;
}
export interface PruneReport {
    provider: string;
    model: string;
    /** The model's agreement with itself, given the full prompt. The yardstick. */
    selfAgreement: number;
    contributions: ExampleContribution[];
    /** Tokens held by examples whose removal changed nothing observable. */
    recoverableTokens: number;
    /** Calls actually made, so the bill is never a surprise. */
    callsMade: number;
}
export interface PruneOptions {
    concurrency?: number;
    countTokens?: TokenCounter;
}
/**
 * What this will cost, before it costs it.
 *
 * Pure and exported so a CLI can print the number and let somebody say no. A
 * feature that spends money and only reports the total afterwards is a feature
 * people run once.
 */
export declare function plannedCalls(examples: number, inputs: number): number;
/**
 * The prompt with one example block removed, located by position rather than by
 * text.
 *
 * `prompt.replace(block.text, '')` would be shorter and wrong: two identical
 * example blocks — which is exactly what a copy-paste few-shot section contains —
 * would both match the first occurrence, so removing the second would silently
 * remove the first and the measurement would describe a prompt nobody asked about.
 * Scanning forward from the end of the previous block gives each block its true
 * offset.
 */
export declare function withoutExample(prompt: string, examples: readonly {
    text: string;
}[], index: number): string;
/** Thrown rather than returned: there is nothing to report and a reason to say. */
export declare class NothingToPrune extends Error {
}
export declare function pruneExamples(prompt: string, inputs: readonly string[], provider: LlmProvider, options?: PruneOptions): Promise<PruneReport>;
//# sourceMappingURL=prune.d.ts.map