A sophisticated serverless application that automatically processes interview recordings, extracts Q&A pairs, and scores candidate responses against job requirements using advanced AI models.
This AWS CDK-based application creates an end-to-end pipeline for analyzing technical interviews in English language. Upload an MP3 recording and get structured insights about the candidate's performance with automatic scoring.
- 🎧 Audio Transcription: High-quality English language transcription with speaker identification
- 🤖 AI-Powered Q&A Extraction: Uses Amazon Bedrock (Claude 4 Sonnet) for single-pass Q&A extraction
- ⚡ Real-time Scoring: Event-driven automatic answer scoring against vacancy requirements
- 🎯 Smart Processing: Single-pass processing handles 1-hour interviews without chunking
- 🔒 Enterprise Security: End-to-end encryption with AWS KMS
- 💰 Optimized Performance: Claude 4 Sonnet with 200k context for superior accuracy
┌─────────────┐ ┌──────────────┐ ┌─────────────────┐ ┌──────────────┐
│ S3 Upload │───▶│ Step Functions│───▶│ Amazon Transcribe│───▶│ Claude 4 │
│ MP3 Files │ │ Orchestration │ │ (English) │ │ Q&A Extract │
└─────────────┘ └──────────────┘ └─────────────────┘ └──────────────┘
│ │
▼ ▼
┌──────────────┐ ┌───────────────────┐ ┌──────────────┐
│ DynamoDB │◀───│ DynamoDB Stream │◀──│ Event-Driven │
│ Q&A Storage │ │ Triggers │ │ Scoring │
└──────────────┘ └───────────────────┘ └──────────────┘
- S3 Bucket: Stores interview recordings and vacancy descriptions
- AWS Transcribe: Converts English audio to text with speaker identification
- Step Functions: Orchestrates the main workflow (transcription → extraction)
- Lambda Functions: Process transcripts, extract Q&A, and score responses
- DynamoDB: Stores structured interview data with automatic scoring
- DynamoDB Streams: Triggers real-time answer scoring
- Amazon Bedrock: Claude 4 Sonnet for Q&A extraction and scoring
├── src/functions/ # Lambda function implementations
│ ├── transcribe_processor/ # Audio transcription (English)
│ ├── qa_extractor/ # Single-pass Q&A extraction
│ └── answer_scorer/ # Real-time answer scoring
├── stacks/ # CDK infrastructure definitions
│ ├── kms_stack.py # Encryption keys
│ ├── s3_interview.py # Storage buckets
│ ├── dynamodb_stack.py # Database tables with streams
│ └── step_functions_stack.py # Workflow orchestration
└── utils/ # Shared utilities and configuration
- Technical Interviews: Analyze coding and system design discussions
- HR Screening: Extract key competencies and responses with automated scoring
- Interview Training: Review and improve interviewing techniques
- Performance Analytics: Track candidate performance across multiple interviews
- Compliance: Maintain structured records with objective scoring
- AWS Account with Bedrock access (Claude 4 Sonnet)
- Python 3.12+
- Poetry for dependency management
- AWS CDK v2
# Clone and setup
git clone <repository>
cd workshop-serverless-applications-for-ai
# Install dependencies
poetry install
# Configure environment
export CDK_ACCOUNT=your-aws-account-id
export CDK_REGION=us-east-1
export CLOUD_ENVIRONMENT=workshop-dev
# Deploy infrastructure
cdk bootstrap --profile your-aws-profile
cdk deploy --all --profile your-aws-profile# Upload vacancy description
aws s3 cp vacancy.txt s3://interview-artifacts/python-senior/vacancy.txt
# Upload interview recording
aws s3 cp interview.mp3 s3://interview-artifacts/python-senior/interview-123.mp3The system automatically:
- Detects new MP3 uploads via S3 events
- Transcribes audio using AWS Transcribe (English)
- Extracts Q&A pairs using Claude 4 Sonnet (single-pass, 200k context)
- Triggers automatic scoring via DynamoDB streams
- Scores each answer (0-10) against job requirements
- Updates records with scores and summaries in real-time
Query processed interviews from DynamoDB:
# Get interview transcript
dynamodb.Table('interview_transcriptions').get_item(
Key={'id': 'interview-123'}
)
# Get Q&A pairs with automatic scores
dynamodb.Table('interview_qa').query(
IndexName='GSI1',
KeyConditionExpression='interview_id = :id',
ExpressionAttributeValues={':id': 'interview-123'}
)
# Example Q&A result with scoring:
{
"id": "qa-uuid",
"question": "Describe your experience with Python frameworks",
"answer": "I have 5 years of experience with Django...",
"answer_score": 8,
"answer_summary": "Strong technical response with specific examples. Shows deep framework knowledge.",
"question_type": "technical",
"processing_status": "scored"
}graph TD
A[MP3 Upload] --> B[S3 Event Trigger]
B --> C[Step Functions Start]
C --> D[Start Transcription]
D --> E[Wait & Check Status]
E --> F{Transcription Complete?}
F -->|No| E
F -->|Yes| G[Single-Pass Q&A Extraction]
G --> H[Store Q&A Pairs]
H --> I[DynamoDB Stream Trigger]
I --> J[Automatic Answer Scoring]
J --> K[Update with Scores]
K --> L[Pipeline Complete]
- Claude 4 Sonnet: 200k context window handles full interviews
- No Chunking: Eliminates boundary issues and context loss
- Better Accuracy: Full interview context for superior Q&A extraction
- Real-time Processing: Scores answers immediately after extraction
- DynamoDB Streams: Automatic triggering without polling
- Parallel Scoring: Each Q&A pair scored independently
- Rate Limiting: Built-in throttling protection for Bedrock API
- Speaker Identification: Distinguishes Interviewer vs Candidate
- Confidence-based Selection: Uses best transcript alternatives
- Question Classification: Categorizes questions by type
- 0-10 Scale: Standardized scoring across all answers
- Context-Aware: Considers position requirements and question type
- Summary Generation: 1-2 sentence quality assessment
- Retry Logic: Handles API throttling with exponential backoff
# Lint code
poetry run flake8
# Format code
poetry run black .
# Run tests
poetry run pytest# Synthesize templates
cdk synth
# Compare changes
cdk diff --all
# Deploy specific stack
cdk deploy WorkshopStepFunctionsStack
# Destroy resources
cdk destroy --all- CloudWatch Logs: Detailed function execution logs with scoring details
- Step Functions Console: Visual workflow monitoring
- DynamoDB Metrics: Storage and query performance
- DynamoDB Streams: Real-time processing monitoring
- Bedrock Usage: Claude 4 Sonnet invocation tracking
- Error Handling: Comprehensive retry and fallback mechanisms
- KMS Encryption: All data encrypted at rest and in transit
- IAM Roles: Least privilege access principles
- Stream Security: Encrypted DynamoDB streams
- Bedrock Governance: Controlled Claude 4 Sonnet access
- Step Functions: Main workflow orchestration
- DynamoDB Streams: Event-driven scoring triggers
- Lambda: Stateless processing functions
- S3 Events: Upload detection and triggering
- Claude 4 Sonnet: Superior reasoning and context understanding
- Single Model: Unified approach for extraction and scoring
- 200k Context: Handles full interview transcripts
- Rate Limiting: Intelligent throttling management
- Sequential Processing: Prevents API throttling
- Batch Size Control: Limits concurrent operations
- Exponential Backoff: Handles temporary failures
- Error Recovery: Graceful degradation with fallback scores
- Technical Accuracy: Correctness of technical information
- Relevance: How well the answer addresses the question
- Depth: Level of detail and insight provided
- Communication: Clarity and structure of the response
- Examples: Specific evidence or examples provided
- 9-10: Excellent - Exceeds expectations with deep insights
- 7-8: Good - Shows competence with solid examples
- 5-6: Average - Meets basic expectations
- 3-4: Below Average - Some issues or gaps
- 0-2: Poor - Major problems or incorrect information
- Fork the repository
- Create a feature branch
- Follow code quality standards
- Test with sample interviews
- Submit a pull request
This project is licensed under the MIT License - see the LICENSE file for details.
Built with ❤️ using AWS CDK, Python, Claude 4 Sonnet, and event-driven serverless architecture