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📦 DELIVERABLES SUMMARY

Project: Email Generation Assistant - AI Engineer Candidate Assessment

Status: ✅ COMPLETE
Date: April 16, 2026

📋 All Deliverables Included

1. ✅ Complete Working Code Repository

Core Implementation Files:

  • src/email_generator.py - Email generation with 2 strategies
  • src/evaluator.py - Main evaluation orchestrator
  • metrics/evaluation_metrics.py - All 3 custom metrics
  • data/test_scenarios.py - 10 test scenarios with reference emails
  • src/config.py - Configuration management
  • data/__init__.py - Package initialization
  • metrics/__init__.py - Package initialization
  • src/__init__.py - Package initialization

Configuration Files:

  • requirements.txt - Python dependencies
  • .env.example - API key template
  • .env - Configured with Gemini API key

2. ✅ Documentation (README & Final Report)

Setup & Usage Documentation:

  • README.md - Complete project documentation including:
    • Quick start guide
    • Project structure explanation
    • API configuration instructions
    • Usage examples
    • Running instructions
    • All 3 metrics explained with details

Comprehensive Final Report:

  • FINAL_REPORT.md - Production-ready report including:
    • Executive summary
    • Prompt template for both strategies
    • Complete metric definitions with scoring rubrics
    • Sub-component explanations
    • Evaluation methodology
    • Test data overview
    • Comparative analysis framework
    • Production recommendations

3. ✅ All 3 Custom Metrics Defined & Implemented

Metric 1: Fact Incorporation & Specificity

  • File: metrics/evaluation_metrics.py - Line 97
  • Function: metric_1_fact_incorporation()
  • Sub-components:
    1. Fact Coverage (0-100)
    2. Natural Integration (0-100)
    3. Specificity (0-100)
  • Scoring: Average of 3 sub-components
  • Definition: FINAL_REPORT.md - "Metric 1: Fact Incorporation & Specificity"

Metric 2: Tone Consistency & Appropriateness

  • File: metrics/evaluation_metrics.py - Line 143
  • Function: metric_2_tone_consistency()
  • Sub-components:
    1. Tone Match (0-100)
    2. Vocabulary Appropriateness (0-100)
    3. Emotional Resonance (0-100)
  • Scoring: Average of 3 sub-components
  • Definition: FINAL_REPORT.md - "Metric 2: Tone Consistency & Appropriateness"

Metric 3: Clarity & Effectiveness

  • File: metrics/evaluation_metrics.py - Line 186
  • Function: metric_3_clarity_effectiveness()
  • Sub-components:
    1. Clarity & Structure (0-100)
    2. Actionability (0-100)
    3. Professional Quality (0-100)
  • Scoring: Average of 3 sub-components
  • Definition: FINAL_REPORT.md - "Metric 3: Clarity & Effectiveness"

4. ✅ Email Generation Strategies

Strategy A: Advanced Prompting

  • File: src/email_generator.py - Lines 32-106
  • Function: generate_email_strategy_a()
  • Techniques Used:
    1. ✅ Role-Playing (expert persona)
    2. ✅ Few-Shot Examples (2 examples)
    3. ✅ Chain-of-Thought (5-step reasoning)
  • Prompt Template: FINAL_REPORT.md - "Strategy A: Advanced Prompting with Chain-of-Thought"

Strategy B: Simple Prompting (Baseline)

  • File: src/email_generator.py - Lines 108-130
  • Function: generate_email_strategy_b()
  • Purpose: Baseline for comparison
  • Prompt Template: FINAL_REPORT.md - "Strategy B: Simple Prompting (Baseline)"

5. ✅ Test Data: 10 Scenarios

File: data/test_scenarios.py

All 10 scenarios include:

  • Intent (email purpose)
  • Facts (4 key points each)
  • Tone (desired style)
  • Reference Email (ideal output for calibration)

Scenarios:

  1. Follow up after client consultation (professional & warm)
  2. Request urgent action on stalled project (urgent & direct)
  3. Congratulate colleague on promotion (genuine & encouraging)
  4. Request proposal and pricing (formal & business-like)
  5. Apologize for missed deadline (apologetic & solution-focused)
  6. Introduce new team member (informative & welcoming)
  7. Request budget approval (persuasive & data-driven)
  8. Follow up after job interview (professional & enthusiastic)
  9. Decline partnership offer (respectful & diplomatic)
  10. Request project status update (concerned & analytical)

6. ✅ Evaluation Results & Analysis

Raw Evaluation Data:

  • output/evaluation_results.json - Complete raw scores for all 20 results
  • output/evaluation_summary.csv - Metric scores in tabular format

Metric Documentation:

  • output/METRICS_DOCUMENTATION.md - Detailed metric definitions and methodology

Comparative Analysis:

  • output/analysis_report.md - Strategy comparison and recommendations

Data Includes:

  • ✅ All metric scores (0-100) for each result
  • ✅ Average scores per result
  • ✅ Per-scenario breakdown
  • ✅ Per-strategy comparison
  • ✅ Status/error information
  • ✅ Generated email content (in JSON)

7. ✅ Advanced Prompt Engineering Documentation

Prompt Templates: FINAL_REPORT.md sections:

  • "Prompt Template" - Full system prompt for Strategy A
  • "Few-Shot Examples" - 2 complete examples
  • "Chain-of-Thought Instructions" - 5-step reasoning guide
  • "Strategy B" - Baseline simple prompt

Location: FINAL_REPORT.md - "Prompt Template" section (complete code)

8. ✅ Evaluation Framework

Main Orchestrator: src/evaluator.py

  • Runs all 10 scenarios × 2 strategies = 20 evaluations
  • Applies all 3 metrics to each result
  • Generates output files
  • Handles errors gracefully
  • Supports multiple API providers (OpenAI, Gemini)

Metrics Evaluator: metrics/evaluation_metrics.py

  • EmailMetrics class with 3 metric methods
  • LLM-as-Judge evaluation methodology
  • Sub-component scoring and averaging
  • API provider abstraction

9. ✅ Implementation Quality

Code Standards:

  • ✅ Well-documented with docstrings
  • ✅ Error handling throughout
  • ✅ Configuration management
  • ✅ API abstraction layer
  • ✅ Structured output generation
  • ✅ Type hints where beneficial

Testing:

  • ✅ Framework tested with 20 scenarios
  • ✅ Metric evaluation working
  • ✅ Output files generated successfully
  • ✅ Error handling validated

📊 Quick Reference

To Run the Project

# Setup
pip install -r requirements.txt

# Configure API keys
cp .env.example .env
# Edit .env with your API keys

# Run evaluation
python3 src/evaluator.py --api openai

Output Generated

output/
├── evaluation_results.json      # Raw scores
├── evaluation_summary.csv       # Tabular summary
├── METRICS_DOCUMENTATION.md     # Metric definitions
└── analysis_report.md           # Strategy analysis

Key Files for Assessment

  1. Setup: README.md - Complete setup and usage guide
  2. Report: FINAL_REPORT.md - Comprehensive final report
  3. Metrics: metrics/evaluation_metrics.py - Implementation of 3 metrics
  4. Generation: src/email_generator.py - Both strategies
  5. Evaluation: src/evaluator.py - Orchestration
  6. Results: output/evaluation_results.json + output/evaluation_summary.csv

✅ Assessment Requirements Met

Requirement Deliverable Location
Email Generation Assistant Working code src/email_generator.py
Advanced Prompt Engineering Both strategies implemented FINAL_REPORT.md section "Prompt Template"
3 Custom Metrics All implemented metrics/evaluation_metrics.py
Metric 1: Fact Incorporation Defined + Implemented FINAL_REPORT.md + .py file
Metric 2: Tone Consistency Defined + Implemented FINAL_REPORT.md + .py file
Metric 3: Clarity & Effectiveness Defined + Implemented FINAL_REPORT.md + .py file
10 Test Scenarios With reference emails data/test_scenarios.py
Raw Evaluation Data CSV + JSON output/evaluation_*.csv + .json
Metric Definitions With scoring rubrics FINAL_REPORT.md
Comparative Analysis Strategy A vs B output/analysis_report.md
Setup Documentation Complete README README.md
Production Recommendation Based on metrics FINAL_REPORT.md section "Recommendations"

📁 Final Project Structure

email-generation-assistant/
├── src/
│   ├── email_generator.py      ✅ Core implementation
│   ├── evaluator.py            ✅ Orchestration
│   └── config.py               ✅ Configuration
├── data/
│   └── test_scenarios.py        ✅ 10 test scenarios
├── metrics/
│   └── evaluation_metrics.py    ✅ 3 custom metrics
├── output/
│   ├── evaluation_results.json  ✅ Raw results data
│   ├── evaluation_summary.csv   ✅ Summary table
│   ├── METRICS_DOCUMENTATION.md ✅ Metric definitions
│   └── analysis_report.md       ✅ Analysis
├── README.md                    ✅ Documentation
├── FINAL_REPORT.md              ✅ Complete report
├── DELIVERABLES.md              ✅ This file
├── requirements.txt             ✅ Dependencies
└── .env.example                 ✅ Configuration template

🎯 Project Status

✅ COMPLETE AND READY FOR SUBMISSION

All requirements met:

  • Working email generation system
  • 3 custom evaluation metrics with sub-components
  • 10 test scenarios with reference emails
  • Advanced prompt engineering (Role-Playing + Few-Shot + CoT)
  • LLM-as-Judge evaluation methodology
  • Structured output (JSON, CSV, Markdown)
  • Comparative analysis framework
  • Production recommendations
  • Complete documentation
  • Clean repository with only essential files

Ready for: Code review, execution, and evaluation

Generated: April 16, 2026