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The evaluation system enables interviewers and team members to score candidates and provide structured feedback at different stages of your hiring pipeline.

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Scoring system

WeGotWork uses a 1-5 scale for candidate evaluations:

1

Poor fit

2

Below average

3

Average

4

Good fit

5

Excellent fit
Each evaluation is tied to a specific pipeline stage, allowing you to track candidate progress and gather feedback from multiple interviewers.

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Creating evaluations

Interviewers can submit evaluations after reviewing or interviewing a candidate: 
const evaluation = await prisma.evaluation.create({
  data: {
    score: 4,
    feedback: "Strong technical skills and great communication. Would be a good fit for the team.",
    interviewerId: session.user.id,
    applicantId: "applicant-id",
    stageId: "stage-id"
  },
  include: {
    stage: true,
    interviewer: true
  }
});
Each evaluation automatically creates an activity log entry to maintain an audit trail.

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Evaluation model

The Evaluation model structure: 
model Evaluation {
  id             String       @id @default(cuid())
  score          Int          // 1-5 scale
  feedback       String?
  interviewerId  String
  interviewer    User         @relation(fields: [interviewerId], references: [id])
  applicantId    String
  applicant      Applicant    @relation(fields: [applicantId], references: [id], onDelete: Cascade)
  stageId        String
  stage          Stage        @relation(fields: [stageId], references: [id], onDelete: Cascade)
  createdAt      DateTime     @default(now())
  updatedAt      DateTime     @updatedAt

  @@index([applicantId])
  @@index([stageId])
  @@map("evaluation")
}

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Retrieving evaluations

View all evaluations for a candidate: 
const applicant = await prisma.applicant.findUnique({
  where: { id: applicantId },
  include: {
    evaluations: {
      include: {
        stage: true,
        interviewer: {
          select: {
            id: true,
            name: true,
            email: true,
            image: true
          }
        }
      },
      orderBy: { createdAt: "desc" }
    }
  }
});

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Interviewer feedback

The feedback field is optional but highly recommended. Detailed feedback helps with:
  • Making informed hiring decisions
  • Providing context for scores
  • Sharing insights with other team members
  • Documenting the interview process

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Feedback best practices

Instead of “good candidate,” write “demonstrated strong problem-solving skills during the coding challenge, completing it 15 minutes ahead of schedule.”
Relate your feedback to the specific skills and qualifications needed for the role.
Reference specific moments from the interview or concrete examples from the candidate’s background.
Avoid personal biases and focus on job-relevant competencies.

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Average scores

Calculate the average score across all evaluations for a candidate: 
const evaluations = await prisma.evaluation.findMany({
  where: { applicantId }
});

const averageScore = evaluations.length > 0
  ? evaluations.reduce((sum, e) => sum + e.score, 0) / evaluations.length
  : 0;

console.log(`Average score: ${averageScore.toFixed(1)}/5`);

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Stage-specific evaluations

You can filter evaluations by pipeline stage to see how candidates performed at different interview phases: 
const technicalInterviewEvals = await prisma.evaluation.findMany({
  where: {
    applicantId,
    stage: {
      name: "Technical Interview"
    }
  },
  include: { interviewer: true }
});
Evaluations are automatically deleted when their associated applicant is removed due to the onDelete: Cascade relationship.

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Activity log integration

When you create an evaluation, an activity log entry is automatically generated: 
await prisma.activityLog.create({
  data: {
    action: "ADDED_EVALUATION",
    actorId: session.user.id,
    applicantId: applicantId,
    metadata: {
      stage: evaluation.stage.name,
      score: evaluation.score
    }
  }
});
See the activity logs documentation for more details on tracking candidate history.

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