Ela MCB

What You'll Learn:

• Prompt Engineering: Real prompts for 10x faster development
• Workflow Integration: Daily development routines with AI
• Advanced Techniques: Chain-of-thought and role-based prompting
• Real Examples: Every technique demonstrated in this portfolio

Real Capabilities:

• Tests 100+ prompt/response pairs across LLM providers
• Measures accuracy, toxicity, and bias automatically
• Detects hallucinations and factual errors
• Prevents prompt injection attacks
• CI/CD pipeline integration for continuous testing

Recent Results:

Input:

AI Output:

test('login with valid credentials', async ({ page }) => {
  await page.goto('/login');
  await page.fill('[data-testid="email"]', 'user@example.com');
  await page.fill('[data-testid="password"]', 'password123');
  await page.click('[data-testid="login-button"]');
  await expect(page).toHaveURL('/dashboard');
});

Input:

AI Output:

• Check if element selector is correct
• Verify page load timing
• Add explicit wait conditions
• Consider dynamic content loading

What's Included:

• Week 1: Core prompting & mental models
• Week 2: Evaluation, data & iteration
• Week 3: Domain transfer & advanced patterns
• Week 4: Portfolio, interview prep & outreach

Key Deliverables:

• Prompt A/B CLI tool
• Evaluation frameworks & metrics
• Case studies with measurable results
• Portfolio site & interview stories

Sources Monitored:

• GitHub: New LLM repositories
• ArXiv: Latest research papers
• Hugging Face: New model releases
• Papers with Code: SOTA updates

Features:

• Automated weekly scans
• Smart filtering for significant releases
• Accumulated discoveries for review
• Centralized discovery dashboard

Key Contributions:

• Hallucination Detection: Consistency-based framework for identifying factual errors
• Bias Analysis: Multi-dimensional approach to measuring unfair responses
• Safety Validation: Comprehensive framework for harmful content detection
• Testing Pipeline: Integrated solution for production LLM validation

Key Innovations:

• Context-Aware Testing: Real-time application state integration
• Dynamic Test Generation: AI-driven test creation based on live data
• Self-Healing Tests: Automatic adaptation to application changes
• Intelligent Debugging: Complete failure context analysis

Key Innovations:

• Multi-Agent Systems: Coordinated autonomous testing workflows
• Specialized Agents: Explorer, Executor, Analyzer, and Orchestrator agents
• Platform Integration: Leveraging AutoGPT, LangChain, and Semantic Kernel
• Autonomous QA: Self-improving testing systems with minimal human oversight

Key Contributions:

• Evaluation Framework: Systematic approach to model assessment across 6 key dimensions
• Benchmark Suite: Test generation, bug detection, and adversarial testing scenarios
• Model Comparisons: Side-by-side analysis of GPT-4, Claude 3.5, CodeLlama, and Gemini
• ROI Calculator: Production metrics and cost-benefit analysis for deployment decisions

Key Insights:

• Healthcare EHR Example: Patient portal with HIPAA compliance requirements
• 7 Agent Types: Explorer, Generator, Security, Compliance, Orchestrator agents
• Proven Results: 92% coverage, 88% faster tests, 487% ROI
• Practical Implementation: Tech stack, adoption roadmap, code examples

Key Findings:

• Manager-Worker Architecture: 80.2% defect detection, 31% cost reduction
• Comparative Analysis: 4 architectures across 5 specialized agent roles
• ATAO Framework: Context-aware architecture selection system
• Statistical Validation: ANOVA, Tukey HSD, effect size analysis