通过爬山法评估优化你的提示词

3:54 - The BookTaggingEvaluation

// MARK: - Evaluation
  struct BookTaggingEvaluation: Evaluation {
      func subject(from sample: ModelSample<BookTags>) async throws -> ModelSubject<BookTags> {
          let result = try await BookTaggingService.generateTags(for: sample.promptDescription)
          return ModelSubject(value: result)
      }

      // MARK: - Dataset
      var dataset = ArrayLoader(samples:
          Book.sampleBooks.map { book in
              ModelSample(prompt: book.review, expected: BookTags(tags: book.tags))
          }
      )

      // MARK: - Evaluators & Metrics
      var tagCount = Metric("Tag Count")
      let hasGenreTag = Metric("Has Genre Tag")
      let noDuplicates = Metric("No Duplicates")

      let relevance = ScoreDimension(
          "Relevance",
          description: """
              Whether each tag describes a quality, theme, or tone of the
              book itself rather than incidental details or the reader's
              personal reactions.
              """,
          scale: .numeric([
              4: "Every tag describes the book itself",
              3: "Most tags describe the book, one picks up a reader reaction or minor detail",
              2: "Most tags are surface details or personal reactions, not book descriptors",
              1: "Tags don't meaningfully describe the book"
          ])
      )

      let usefulness = ScoreDimension(
          "Usefulness",
          description: """
              Whether tags are at the right granularity for browsing — broad
              enough that multiple books could share the tag, specific enough
              to help filter.
              """,
          scale: .numeric([
              4: "Every tag could group multiple books while still narrowing a search",
              3: "Most tags are at the right level, one is either too broad or too narrow",
              2: "Most tags are too broad to filter or too narrow to group",
              1: "Tags would not help with browsing"
          ])
      )

      var evaluators: Evaluators {
          // 1. Tag count is within the required 3–8 range
          Evaluator { _, subject in
              let count = subject.value.tags.count
              if (count >= 3 && count <= 8) {
                  return tagCount.passing(rationale: "\(count) tags")
              }
              return tagCount.failing(rationale: "Got \(count) tags, expected 3–8")
          }
  
          // 2. At least one tag identifies the genre or literary form
          Evaluator { _, subject in
              let tags = subject.value.tags.map { $0.lowercased() }
              let knownGenres = await BookTaggingService.knownGenres
              for tag in tags {
                  if knownGenres.contains(tag) {
                      return hasGenreTag.passing(rationale: "Matched \(tag)")
                  }
              }
              return hasGenreTag.failing()
          }

          // 3. No duplicate tags
          Evaluator { _, subject in
              let uniqueCount = Set(subject.value.tags.map { $0.lowercased() }).count
              if (subject.value.tags.count - uniqueCount) > 0 {
                  return noDuplicates.failing(rationale: "Found \(subject.value.tags.count - uniqueCount) duplicates")
              }
              return noDuplicates.passing()
          }
  
          // 4. Overall tag quality — groundedness, coverage, specificity
          ModelJudgeEvaluator(
              judge: .default,
              dimensions: [relevance, usefulness],
              prompt: ModelJudgePrompt(
                  instructions: """
                      You are evaluating automatically generated tags for Shelf, a personal
                      book tracking app. Users write a short summary of their reading
                      experience, and the app generates tags to make their library browsable.
                      A good tag describes the book itself — its genre, themes, tone, or
                      setting. A bad tag picks up incidental details or the reader's personal
                      reactions that don't describe the book.
                      """,
                  evaluationTarget: { output in output.tags.joined(separator: ", ") },
                  reference: { input, _ in
                      ["Expected Tags": input.expected?.tags.joined(separator: ", ") ?? ""]
                  }
              )
          )
      }

      // MARK: - Analysis
      func aggregateMetrics(using aggregator: inout MetricsAggregator) {
          aggregator.group("Heuristics") { group in
              group.computeMean(of: tagCount)
              group.computeMean(of: hasGenreTag)
              group.computeMean(of: noDuplicates)
          }
          aggregator.group("Quality") { group in
              group.computeMean(of: relevance.metric)
              group.computeMean(of: usefulness.metric)
          }
      }
  }

4:05 - Refined Relevance & Usefulness score dimensions

let relevance = ScoreDimension(
      "Relevance",
      description: """
          Whether each tag describes the book itself — its genre, themes,
          tone, or setting — rather than the reader's reactions, meta-
          commentary about the review, or facts about the author. A book
          can be "suspenseful" (a property of the text); a reader is
          "exhausted" (a reaction). Mis-labeling the genre is a serious failure.
          """,
      scale: .numeric([
          4: "Every tag describes the book itself",
          3: "Most tags describe the book, one picks up a reader reaction or minor detail",
          2: "Most tags are surface details or personal reactions, not book descriptors",
          1: "Tags don't meaningfully describe the book"
      ])
  )

  let usefulness = ScoreDimension(
      "Usefulness",
      description: """
          Whether tags work as library shelf labels — broad enough that
          several books could plausibly share the tag, specific enough to
          meaningfully narrow a search. Standard genre and theme tags work;
          made-up phrases, character names, hyper-specific descriptors, and
          overly generic words like "interesting" don't.
          """,
      scale: .numeric([
          4: "Every tag could group multiple books while still narrowing a search",
          3: "Most tags are at the right level, one is either too broad or too narrow",
          2: "Most tags are too broad to filter or too narrow to group",
          1: "Tags would not help with browsing"
      ])
  )

11:56 - The alignment dataset, extracted to JSON

// Model judge alignment dataset
  [
    {
      "input": "I have read this book more times than I can count…",
      "response": "[\"literary-fiction\", \"historical-fiction\", \"family-drama\", \"romantic-drama\", 
  \"character-driven\", \"emotional-intensity\", \"multigenerational-narrative\", \"penned-by-a-woman\"]"
    }
    // ... add your expert ratings to each entry
  ]

12:31 - The judge alignment evaluation: dataset, subject, evaluator

// Model judge alignment evaluation
  struct BookTagJudgmentCalibration: Evaluation {

      // MARK: Dataset — load the extracted summary/tag pairs
      static let samples: [ModelSample<BookTagJudgmentValue>] = {
          guard let url = Bundle(for: BundleToken.self).url(
                  forResource: "BookTaggingEvaluation-extracted", withExtension: "json"),
                let data = try? Data(contentsOf: url) else { return [] }
          // Build ModelSample array (adding expert ratings)
          // ...
      }()

      var dataset: some Loader { ArrayLoader(samples: Self.samples) }
  
      // MARK: Capture Subject — tags are already generated, so just return them
      func subject(from sample: ModelSample<BookTagJudgmentValue>) async throws -> ModelSubject<BookTagJudgmentValue> {
          ModelSubject(value: sample.expected ?? BookTagJudgmentValue(
              tags: [], expertRelevanceScore: 0, expertUsefulnessScore: 0))
      }

      // MARK: Evaluators — the same model judge as the book-tags evaluation
      var evaluators: Evaluators {
          ModelJudgeEvaluator(
              judge: .default,
              dimensions: [relevance, usefulness],
              prompt: ModelJudgePrompt(
                  instructions: "You are evaluating automatically generated tags for Book Tracker…",
                  evaluationTarget: { output in output.tags.joined(separator: ", ") },
                  reference: { input, _ in
                      ["Expected Tags": input.expected?.tags.joined(separator: ", ") ?? ""]
                  }
              )
          )
      }
  }

13:00 - Cohen's kappa aggregation

func aggregateMetrics(using aggregator: inout MetricsAggregator) {
      let expertRelevance = Self.samples.map { Double($0.expected?.expertRelevanceScore ?? 0) }
      let expertUsefulness = Self.samples.map { Double($0.expected?.expertUsefulnessScore ?? 0) }

      aggregator.group("Relevance") { group in
          group.computeMean(of: relevance.metric)
          group.computeStandardDeviation(of: relevance.metric)
          group.custom(of: relevance.metric, label: "Relevance Alignment Score") { judge in
              cohensKappa(ratings1: expertRelevance, ratings2: judge) ?? 0
          }
      }
      aggregator.group("Usefulness") { group in
          group.computeMean(of: usefulness.metric)
          group.computeStandardDeviation(of: usefulness.metric)
          group.custom(of: usefulness.metric, label: "Usefulness Alignment Score") { judge in
              cohensKappa(ratings1: expertUsefulness, ratings2: judge) ?? 0
          }
      }
  }

13:24 - The judge calibration test

// Model judge alignment tests
  @Suite("Book Tag Judge Calibration")
  struct BookTagJudgmentCalibrationTests {
      static let evaluation = BookTagJudgmentCalibration()

      @Test("Judge Calibration", .evaluates(evaluation))
      func evaluateJudgeCalibration() async throws {
          let result = EvaluationContext.current.result

          let usefulnessMetric = BookTagJudgmentCalibrationTests.evaluation.usefulness.metric
          let relevanceMetric = BookTagJudgmentCalibrationTests.evaluation.relevance.metric

          #expect(result.aggregateValue(.custom(label: "Relevance: Judge vs Expert")) > 0.6)
          #expect(result.aggregateValue(.custom(label: "Usefulness: Judge vs Expert")) > 0.6)
      }
  }

16:33 - The experimental judge prompt

// Experimental evaluation
  struct BookTagJudgmentCalibrationExperimental: Evaluation {
      var evaluators: Evaluators {
          ModelJudgeEvaluator(
              judge: .default,
              dimensions: [relevance, usefulness],
              prompt: ModelJudgePrompt(
                  instructions: """
                      You are an experienced reader and librarian evaluating tags
                      automatically generated for Book Tracker... Score the tag set on two
                      independent dimensions: Relevance and Usefulness.

                      ## What a good tag looks like
                      - Genre/form, theme/subject, tone/atmosphere, setting/era

                      ## Common failure modes
                      - Reader reactions, meta-commentary, author facts, genre contradictions
                      """,   // ← full prompt is ~40 lines; abbreviated here
                  evaluationTarget: { output in output.tags.joined(separator: ", ") },
                  reference: { input, _ in
                      ["Book Review": input.promptDescription,
                       "Tags Generated for the Review": input.expected?.tags.joined(separator: ", ") ?? ""]
                  }
              )
          )
      }
  }

20:12 - Few-shot worked examples in the judge prompt

struct ExperimentalBookTagJudgmentCalibration: Evaluation {
      var evaluators: Evaluators {
          ModelJudgeEvaluator(
              judge: SystemLanguageModel(),
              dimensions: [relevance, usefulness],
              prompt: ModelJudgePrompt(
                  instructions: """
                      You are calibrating with an expert librarian who scores
                      automatically generated tags for Book Tracker... Your goal is to
                      match how the librarian scores. Use the worked examples to calibrate.

                      ## Worked examples
                      ### Example A — clean fit (Pride and Prejudice)
                      Tags: romance, historical-fiction, love, redemption, passion
                      Librarian: Relevance 4, Usefulness 4

                      ### Example E — flat genre contradiction (Frankenstein)
                      Tags: horror, science-fiction, ... self-help, self-improvement
                      Librarian: Relevance 2, Usefulness 3
                      ... (6 examples A–F; keep the set small to avoid overfitting)
                      """,   // ← full prompt is ~60 lines; abbreviated here
                  evaluationTarget: { output in output.tags.joined(separator: ", ") },
                  reference: { input, _ in
                      ["Book Review": input.promptDescription,
                       "Tags Generated for the Review": input.expected?.tags.joined(separator: ", ") ?? ""]
                  }
              )
          )
      }
  }

  9. The BookLookupTool — slides 166–167

22:03 - The BookLookupTool

// Book Information Lookup Tool
  struct BookLookupTool: Tool {
      let name = "lookupBook"
      let description = "Looks up the title and author of a book given distinguishing details — such as character names, 
  settings, quoted lines, or notable plot points — extracted from a reader's review."

      @Generable
      struct Arguments {
          @Guide(description: "Distinguishing details from the review that identify the book, such as character names, 
  settings, quoted lines, or notable plot points.")
          var details: String
      }
  
      @Generable
      struct Output {
          @Guide(description: "The title of the identified book, or an empty string if no match was found.")
          var title: String

          @Guide(description: "The author of the identified book, or an empty string if no match was found.")
          var author: String
      }
  
      func call(arguments: Arguments) async throws -> Output {
          let needles = arguments.details
              .lowercased()
              .split(whereSeparator: { !$0.isLetter && !$0.isNumber })
              .map(String.init)
              .filter { $0.count >= 4 }

          let best = Book.sampleBooks
              .map { book -> (book: Book, score: Int) in
                  let review = book.review.lowercased()
                  let score = needles.reduce(0) { partial, needle in
                      partial + (review.contains(needle) ? 1 : 0)
                  }
                  return (book, score)
              }
              .max(by: { $0.score < $1.score })

          guard let match = best, match.score > 0 else {
              return Output(title: "", author: "")
          }
          return Output(title: match.book.title, author: match.book.author)
      }
  }

22:36 - BookTaggingService with a tools parameter

// Book Tagging Service
  struct BookTaggingService {
      static func generateTags(for review: String, tools: [any Tool] = []) async throws -> BookTags {
          let prompt = tagsPrompt(review: review)
          let session = LanguageModelSession(
              model: SystemLanguageModel(guardrails: .permissiveContentTransformations),
              tools: tools,
              instructions: instructions
          )
          let response = try await session.respond(to: prompt, generating: BookTags.self)
          return response.content
      }
  }

22:57 - Evaluation with the lookup tool

// Evaluation of tags with tool
  struct BookTaggingWithLookupEvaluation: Evaluation {
      func subject(from sample: ModelSample<BookTags>) async throws -> ModelSubject<BookTags> {
          let result = try await BookTaggingService.generateTags(
              for: sample.promptDescription,
              tools: [BookLookupTool()]
          )
          return ModelSubject(value: result)
      }
      // ... same dataset, evaluators, and aggregation as BookTaggingEvaluation
  }

23:09 - Compare with/without the tool in one suite

@Suite("Book Tag Evaluations")
  struct BookTagEvaluationTests {
      static let evaluation = BookTaggingEvaluation()
      static let lookupEvaluation = BookTaggingWithLookupEvaluation()

      @Test("Book Tag Evaluations", .evaluates(evaluation, info: evaluationInfo))
      func evaluateBookTagging() async throws {
          let result = EvaluationContext.current.result
          let rangeMetric = BookTagEvaluationTests.evaluation.tagCount
          let dupeMetric = BookTagEvaluationTests.evaluation.noDuplicates
          #expect(result.aggregateValue(.mean(of: rangeMetric)) >= 0.8)
          #expect(result.aggregateValue(.mean(of: dupeMetric)) == 1)
      }

      @Test("Book Tag Evaluations (with BookLookupTool)", .evaluates(lookupEvaluation, info: lookupEvaluationInfo))
      func evaluateBookTaggingWithLookup() async throws {
          let result = EvaluationContext.current.result
          let rangeMetric = BookTagEvaluationTests.lookupEvaluation.tagCount
          let dupeMetric = BookTagEvaluationTests.lookupEvaluation.noDuplicates
          #expect(result.aggregateValue(.mean(of: rangeMetric)) >= 0.8)
          #expect(result.aggregateValue(.mean(of: dupeMetric)) == 1)
      }
  }