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After completing your mock interview, you receive detailed AI-powered feedback that analyzes your performance and provides specific guidance for improvement.

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Overview

The feedback analysis evaluates your interview responses across multiple dimensions and delivers a comprehensive report including:
  • An overall performance score (1-10)
  • Specific strengths you demonstrated
  • Areas that need improvement
  • CV optimization suggestions for the target role

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Feedback structure

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Overall performance score

You receive a numerical score from 1 to 10 that reflects your overall interview performance. 
{
  "score": 7,
  "strengths": "...",
  "weaknesses": "...",
  "cv_improvements": "..."
}
The score considers:
  • Relevance of answers - How well your responses addressed the questions
  • Communication clarity - How effectively you articulated your thoughts
  • Technical depth - How thoroughly you demonstrated required skills
  • Alignment with job requirements - How well you matched what the role needs
The score is displayed prominently with a visual indicator showing filled circles for achieved points out of 10.

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Strengths analysis

This section highlights 3-5 specific aspects of your interview performance that were strong, including:
  • Skills you demonstrated effectively
  • Well-articulated responses
  • Relevant experience you showcased
  • Specific examples from your answers that impressed
Example feedback: 
• Strong communication skills demonstrated throughout the interview
• Excellent use of specific metrics when describing project outcomes
• Clear understanding of modern backend development practices
• Good alignment between your experience and the role requirements
• Confident presentation of technical problem-solving abilities

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Areas for improvement

The AI identifies 3-5 specific aspects where your interview could be stronger:
  • Where answers lacked sufficient depth
  • Skills that needed more demonstration
  • Topics that could have been explored further
  • Missed opportunities to showcase relevant experience
Example feedback: 
• Could provide more specific examples of stakeholder communication
• Technical explanations would benefit from more detail about implementation
• Missed opportunity to discuss your experience with cloud technologies
• Answers about team collaboration could be more structured (STAR method)
• Would strengthen responses by mentioning quantifiable business impact
Each improvement area is specific and actionable, telling you exactly what to focus on for your next interview.

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CV optimization recommendations

See the CV optimization page for detailed information about this section.

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How feedback is generated

The AI analyzes multiple data points to create your feedback:
1

Transcript analysis

The complete interview conversation is reviewed:
  • All questions asked by the AI interviewer
  • All answers you provided
  • The flow and progression of the conversation
2

CV comparison

Your CV content is compared against your interview responses to identify:
  • Skills mentioned in CV but not discussed in interview
  • Experience you talked about but didn’t emphasize in your CV
  • Alignment between written and verbal presentation
3

Job requirements matching

Your responses are evaluated against the job description to assess:
  • Coverage of required skills and qualifications
  • Use of relevant terminology and concepts
  • Understanding of role responsibilities
4

Comprehensive scoring

All factors are synthesized into:
  • A single performance score
  • Categorized strengths and weaknesses
  • Tailored CV improvement suggestions

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Viewing your feedback

The feedback page displays your results in an organized, visually appealing format:

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Score card

A prominent gradient banner shows your overall score with:
  • Large numerical display (e.g., “7/10”)
  • Visual indicator with filled circles
  • “Overall Performance” label

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Side-by-side comparison

Strengths and improvement areas are displayed in adjacent panels:
  • Green-themed card for strengths
  • Orange-themed card for areas to improve
  • Equal visual weight to emphasize both positive and developmental feedback

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CV improvements section

A dedicated purple-themed section at the bottom provides:
  • Clear heading: “CV Improvement Suggestions”
  • Subtitle: “Tailored recommendations for this specific role”
  • Bulleted list of specific actions to take

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Export options

Buttons at the bottom allow you to:
  • Start a new interview session
  • Print or save feedback as PDF for future reference

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Feedback generation process

The system uses a comprehensive prompt to ensure thorough analysis: 
class FeedbackService:
    def generate_feedback(self, session_id: int) -> Feedback:
        # Get conversation history
        conversation_history = self.message_repo.conversation_to_history(session_id)
        
        if not conversation_history:
            raise ValidationError(
                "Cannot generate feedback for an empty interview."
            )
        
        # Generate AI feedback
        feedback_data = self.ai_client.generate_feedback(
            convo_history=conversation_history,
            cv_text=session.cv_text,
            job_desc=session.job_description_text,
            job_title=session.job_title,
        )
        
        # Store in database
        return self.feedback_repo.create_feedback(
            session_id=session_id, 
            **feedback_data
        )

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Understanding your score

Score RangeInterpretation
9-10Exceptional - Interview ready for this role
7-8Strong - Minor improvements needed
5-6Good foundation - Focus on key areas identified
3-4Needs improvement - Practice specific skills highlighted
1-2Significant work needed - Review all feedback carefully
Your score is relative to the specific job requirements. A lower score doesn’t mean you’re a bad candidate - it means you need to better align your presentation with this particular role.

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Best practices

  1. Read through all sections carefully
  2. Note patterns in the improvement areas
  3. Practice addressing weaknesses in future mock interviews
  4. Update your CV based on optimization suggestions
  5. Review strengths to understand what to maintain
Yes! Use the feedback to:
  • Prepare better answers for weak areas
  • Update your CV with suggested improvements
  • Practice articulating your experience more clearly
  • Then create a new session and try again
You can track improvement across multiple sessions.
Absolutely. Use the “Print/Save as PDF” button to:
  • Save feedback for your records
  • Share with mentors or career coaches
  • Compare results across different interview sessions
  • Track your progress over time
The feedback is generated by advanced AI models trained on interview best practices. However:
  • Use it as guidance, not absolute truth
  • Combine with human feedback from mentors
  • Consider it one data point in your preparation
  • Focus on actionable suggestions rather than just the score
The AI provides one perspective based on:
  • Your written responses
  • Job description requirements
  • Common interview best practices
If you disagree:
  • Consider whether the AI identified something you missed
  • Seek a second opinion from a human interviewer
  • Focus on suggestions that resonate with you
  • Try another interview to see if feedback is consistent

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Technical validation

The system ensures feedback quality through validation: 
def generate_feedback(self, convo_history, cv_text, job_desc, job_title) -> dict:
    feedback = self._parse_json(self._generate(prompt), expect_list=False)
    
    # Validate required fields
    required = {"score", "strengths", "weaknesses", "cv_improvements"}
    missing = required - feedback.keys()
    if missing:
        raise AIServiceError(f"Feedback missing keys: {missing}")
    
    # Validate score range
    score = feedback.get("score")
    if not isinstance(score, int) or not (1 <= score <= 10):
        raise AIServiceError(f"Invalid score: {score}")
    
    return feedback
This ensures every feedback report contains:
  • All required sections
  • A valid numeric score
  • Properly formatted content

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Database storage

Your feedback is permanently saved and can be accessed anytime: 
CREATE TABLE feedback (
    id INTEGER PRIMARY KEY,
    session_id INTEGER NOT NULL REFERENCES sessions(id),
    interview_score INTEGER,
    strengths TEXT,
    weaknesses TEXT,
    cv_improvements TEXT,
    created_at DATETIME DEFAULT CURRENT_TIMESTAMP
);
This allows you to:
  • Review past feedback
  • Compare performance across sessions
  • Track improvement over time
  • Reference specific suggestions when preparing for real interviews

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