Pass tag confidence scores to AI assignment for weighted matching

The AI assignment path was receiving project tags as flat strings, losing
the confidence scores from AI tagging. Now both the GPT path and the
fallback algorithm weight tag matches by confidence — a 0.9 tag matters
more than a 0.5 one.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

src/server/routers/assignment.ts

@@ -74,10 +74,22 @@ async function runAIAssignmentJob(jobId: string, roundId: string, userId: string
         description: true,
         tags: true,
         teamName: true,
+        projectTags: {
+          select: { tag: { select: { name: true } }, confidence: true },
+        },
         _count: { select: { assignments: { where: { roundId } } } },
       },
     })
 
+    // Enrich projects with tag confidence data for AI matching
+    const projectsWithConfidence = projects.map((p) => ({
+      ...p,
+      tagConfidences: p.projectTags.map((pt) => ({
+        name: pt.tag.name,
+        confidence: pt.confidence,
+      })),
+    }))
+
     const existingAssignments = await prisma.assignment.findMany({
       where: { roundId },
       select: { userId: true, projectId: true },
@@ -124,7 +136,7 @@ async function runAIAssignmentJob(jobId: string, roundId: string, userId: string
 
     const result = await generateAIAssignments(
       jurors,
-      projects,
+      projectsWithConfidence,
       constraints,
       userId,
       roundId,

src/server/services/ai-assignment.ts

@@ -38,7 +38,7 @@ const ASSIGNMENT_SYSTEM_PROMPT = `You are an expert jury assignment optimizer fo
 Match jurors to projects based on expertise alignment, workload balance, and coverage requirements.
 
 ## Matching Criteria (Weighted)
-- Expertise Match (50%): How well juror tags/expertise align with project topics
+- Expertise Match (50%): How well juror tags/expertise align with project topics. Project tags include a confidence score (0-1) — weight higher-confidence tags more heavily as they are more reliably assigned. A tag with confidence 0.9 is a strong signal; one with 0.5 is uncertain.
 - Workload Balance (30%): Distribute assignments evenly; prefer jurors below capacity
 - Minimum Target (20%): Prioritize jurors who haven't reached their minimum assignment count
 
@@ -99,6 +99,7 @@ interface ProjectForAssignment {
   title: string
   description?: string | null
   tags: string[]
+  tagConfidences?: Array<{ name: string; confidence: number }>
   teamName?: string | null
   _count?: {
     assignments: number
@@ -539,7 +540,7 @@ export function generateFallbackAssignments(
 
         return {
           juror,
-          score: calculateExpertiseScore(juror.expertiseTags, project.tags),
+          score: calculateExpertiseScore(juror.expertiseTags, project.tags, project.tagConfidences),
           loadScore: calculateLoadScore(currentLoad, maxLoad),
           underMinBonus: calculateUnderMinBonus(currentLoad, minTarget),
         }
@@ -586,24 +587,44 @@ export function generateFallbackAssignments(
 
 /**
  * Calculate expertise match score based on tag overlap
+ * When tagConfidences are available, weights matches by confidence
  */
 function calculateExpertiseScore(
   jurorTags: string[],
-  projectTags: string[]
+  projectTags: string[],
+  tagConfidences?: Array<{ name: string; confidence: number }>
 ): number {
   if (jurorTags.length === 0 || projectTags.length === 0) {
     return 0.5 // Neutral score if no tags
   }
 
   const jurorTagsLower = new Set(jurorTags.map((t) => t.toLowerCase()))
+
+  // If we have confidence data, use weighted scoring
+  if (tagConfidences && tagConfidences.length > 0) {
+    let weightedMatches = 0
+    let totalWeight = 0
+
+    for (const tc of tagConfidences) {
+      totalWeight += tc.confidence
+      if (jurorTagsLower.has(tc.name.toLowerCase())) {
+        weightedMatches += tc.confidence
+      }
+    }
+
+    if (totalWeight === 0) return 0.5
+
+    const weightedRatio = weightedMatches / totalWeight
+    const hasExpertise = weightedMatches > 0 ? 0.2 : 0
+    return Math.min(1, weightedRatio * 0.8 + hasExpertise)
+  }
+
+  // Fallback: unweighted matching using flat tags
   const matchingTags = projectTags.filter((t) =>
     jurorTagsLower.has(t.toLowerCase())
   )
 
-  // Score based on percentage of project tags matched
   const matchRatio = matchingTags.length / projectTags.length
-
-  // Boost for having expertise, even if not all match
   const hasExpertise = matchingTags.length > 0 ? 0.2 : 0
 
   return Math.min(1, matchRatio * 0.8 + hasExpertise)

src/server/services/anonymization.ts

@@ -52,7 +52,7 @@ export interface AnonymizedProject {
   anonymousId: string
   title: string
   description: string | null
-  tags: string[]
+  tags: Array<{ name: string; confidence: number }>
   teamName: string | null
 }
 
@@ -209,6 +209,7 @@ interface ProjectInput {
   title: string
   description?: string | null
   tags: string[]
+  tagConfidences?: Array<{ name: string; confidence: number }>
   teamName?: string | null
 }
 
@@ -253,7 +254,9 @@ export function anonymizeForAI(
         description: project.description
           ? truncateAndSanitize(project.description, DESCRIPTION_LIMITS.ASSIGNMENT)
           : null,
-        tags: project.tags,
+        tags: project.tagConfidences && project.tagConfidences.length > 0
+          ? project.tagConfidences
+          : project.tags.map((t) => ({ name: t, confidence: 1.0 })),
         teamName: project.teamName ? `Team ${index + 1}` : null,
       }
     }
@@ -524,7 +527,7 @@ export function validateAnonymization(data: AnonymizationResult): boolean {
     if (!checkText(project.title)) return false
     if (!checkText(project.description)) return false
     for (const tag of project.tags) {
-      if (!checkText(tag)) return false
+      if (!checkText(typeof tag === 'string' ? tag : tag.name)) return false
     }
   }