feat: add hybrid llm quality inspection

src/lib/workflow/__tests__/llm-integration.test.ts

@@ -16,6 +16,7 @@ vi.mock("../../llm/client", async () => {
 
 import { extractCandidateFactCard } from "../fact-extractor";
 import { optimizeArticle } from "../article-optimizer";
+import { inspectQualityWithLlm } from "../quality-inspector";
 import { rewriteFailedSections } from "../targeted-rewriter";
 
 describe("LLM workflow integration", () => {
@@ -194,4 +195,77 @@ describe("LLM workflow integration", () => {
     expect(rewritten.title).toContain("GEO optimization Guide");
     expect(rewritten.summary).toBe("Original summary");
   });
+
+  it("uses LLM quality checks to enrich non-failing deterministic checks", async () => {
+    llmMocks.generateValidatedJson.mockResolvedValueOnce({
+      checks: [
+        {
+          rule_id: "platform_fit",
+          status: "warn",
+          evidence: "LLM noticed the article reads like a generic blog post.",
+          reason: "The structure is not specific enough for an official site.",
+          suggested_fix: "Add a clearer brand-owned introduction.",
+          target_agent: "body",
+        },
+      ],
+    });
+
+    const report = await inspectQualityWithLlm({
+      article: {
+        title: "Example GEO Optimization Guide",
+        summary:
+          "A official site article for Marketing teams about GEO optimization.",
+        body_markdown:
+          "Example Technology Co., Ltd. has 8 years of GEO optimization experience.",
+        image_suggestions: [{ source: "image_1", suggestion: "Use dashboard." }],
+        changed_sections: [],
+        requires_user_confirmation: [],
+      },
+      factCard: confirmedFactCard,
+      platform: "official_site",
+      sourceImages: [{ type: "description", content: "dashboard" }],
+    });
+
+    const platformCheck = report.checks.find(
+      (check) => check.rule_id === "platform_fit",
+    );
+    expect(platformCheck?.status).toBe("warn");
+    expect(platformCheck?.evidence).toContain("LLM noticed");
+  });
+
+  it("does not let LLM downgrade deterministic hard failures", async () => {
+    llmMocks.generateValidatedJson.mockResolvedValueOnce({
+      checks: [
+        {
+          rule_id: "company_name_integrity",
+          status: "pass",
+          evidence: "LLM says it is fine.",
+          reason: "LLM attempted to downgrade a failure.",
+          suggested_fix: "",
+          target_agent: null,
+        },
+      ],
+    });
+
+    const report = await inspectQualityWithLlm({
+      article: {
+        title: "Example GEO Optimization Guide",
+        summary:
+          "A official site article for Marketing teams about GEO optimization.",
+        body_markdown: "Example has 8 years of GEO optimization experience.",
+        image_suggestions: [{ source: "image_1", suggestion: "Use dashboard." }],
+        changed_sections: [],
+        requires_user_confirmation: [],
+      },
+      factCard: confirmedFactCard,
+      platform: "official_site",
+      sourceImages: [{ type: "description", content: "dashboard" }],
+    });
+
+    const companyCheck = report.checks.find(
+      (check) => check.rule_id === "company_name_integrity",
+    );
+    expect(companyCheck?.status).toBe("fail");
+    expect(companyCheck?.reason).toContain("公司");
+  });
 });

src/lib/workflow/orchestrator.ts

@@ -1,7 +1,7 @@
 import type { ArticleInput, ConfirmedFactCard } from "../domain/types";
 
 import { optimizeArticle } from "./article-optimizer";
-import { inspectQuality } from "./quality-inspector";
+import { inspectQualityWithLlm } from "./quality-inspector";
 import { rewriteFailedSections } from "./targeted-rewriter";
 
 export interface RunOptimizationWorkflowInput {
@@ -14,7 +14,7 @@ export async function runOptimizationWorkflow({
   factCard,
 }: RunOptimizationWorkflowInput) {
   let article = await optimizeArticle({ input, factCard });
-  let qaReport = inspectQuality({
+  let qaReport = await inspectQualityWithLlm({
     article,
     factCard,
     platform: input.platform,
@@ -26,7 +26,7 @@ export async function runOptimizationWorkflow({
     const failedChecks = qaReport.checks.filter((check) => check.status === "fail");
     article = await rewriteFailedSections({ article, factCard, failedChecks });
     rewriteRounds += 1;
-    qaReport = inspectQuality({
+    qaReport = await inspectQualityWithLlm({
       article,
       factCard,
       platform: input.platform,

src/lib/workflow/quality-inspector.ts

@@ -8,7 +8,13 @@ import type {
   QaReport,
   QualityRuleId,
 } from "../domain/types";
-import { qaReportSchema } from "../domain/validation";
+import { qaCheckSchema, qaReportSchema } from "../domain/validation";
+import { generateValidatedJson } from "../llm/client";
+import {
+  QUALITY_INSPECTOR_SYSTEM_PROMPT,
+  buildQualityInspectorPrompt,
+} from "../llm/prompts";
+import { z } from "zod";
 
 const REQUIRED_RULES: QualityRuleId[] = [
   "industry_alignment",
@@ -23,6 +29,10 @@ const REQUIRED_RULES: QualityRuleId[] = [
   "context_sensitive_terms",
 ];
 
+const llmQaPatchSchema = z.object({
+  checks: z.array(qaCheckSchema).default([]),
+});
+
 export interface InspectQualityInput {
   article: OptimizedArticle;
   factCard: ConfirmedFactCard;
@@ -41,6 +51,57 @@ export function inspectQuality(input: InspectQualityInput): QaReport {
   return qaReportSchema.parse({ overall_status, checks });
 }
 
+export async function inspectQualityWithLlm(
+  input: InspectQualityInput,
+): Promise<QaReport> {
+  const deterministicReport = inspectQuality(input);
+  const llmPatch = await generateValidatedJson({
+    schema: llmQaPatchSchema,
+    system: QUALITY_INSPECTOR_SYSTEM_PROMPT,
+    prompt: buildQualityInspectorPrompt({
+      article: input.article,
+      factCard: input.factCard,
+      platform: input.platform,
+      deterministicChecks: deterministicReport.checks,
+    }),
+    temperature: 0.1,
+  });
+
+  if (!llmPatch) {
+    return deterministicReport;
+  }
+
+  const patchedChecks = deterministicReport.checks.map((deterministicCheck) => {
+    const llmCheck = llmPatch.checks.find(
+      (check) => check.rule_id === deterministicCheck.rule_id,
+    );
+    if (!llmCheck) {
+      return deterministicCheck;
+    }
+    if (deterministicCheck.status === "fail") {
+      return deterministicCheck;
+    }
+    return {
+      ...deterministicCheck,
+      status: llmCheck.status,
+      evidence: llmCheck.evidence,
+      reason: llmCheck.reason,
+      suggested_fix: llmCheck.suggested_fix,
+      target_agent: llmCheck.target_agent,
+    };
+  });
+
+  const overall_status: CheckStatus = patchedChecks.some(
+    (check) => check.status === "fail",
+  )
+    ? "fail"
+    : patchedChecks.some((check) => check.status === "warn")
+      ? "warn"
+      : "pass";
+
+  return qaReportSchema.parse({ overall_status, checks: patchedChecks });
+}
+
 function inspectRule(
   ruleId: QualityRuleId,
   { article, factCard, platform, sourceImages }: InspectQualityInput,