Section outline

• Select section Day 1: Foundations and Analysis

  Day 1: Foundations and Analysis

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  • undamentals of AI and Gen AI: Introduction to AI, ML, Deep Learning, and LLMs.
  • Prompt Engineering: Crafting effective prompts for AI models, with SDLC examples.
  • AI for Analysis: Streamline software requirements analysis, gap validation, and ambiguity reduction.
  • Complex Code Understanding: Navigate and document brownfield project codebases effectively.
• Select section Day 2: Code and Testing Automation

  Day 2: Code and Testing Automation

  • Code Generation: Automate boilerplate code and templates for consistency and efficiency.
  • AI Agents: Automate tasks like quality checks, testing, and documentation updates.
  • Bug Detection and Fixing: Use AI to identify vulnerabilities and suggest patches.
  • Automated Testing: Generate unit tests, E2E tests, and automate scripts.
  • Code Refactoring: Improve readability, modularity, and maintainability using AI.
• Select section Day 3: Advanced Implementations

  Day 3: Advanced Implementations

  • Private LLM Deployment: Secure SDLC workflows with private AI models.
  • RAG Techniques: Maintain dynamic and current project documentation.
  • Technical Debt Management: Prioritize and resolve legacy code challenges.
  • Gen AI in DevOps: Optimize workflows and accelerate delivery cycles.
  • LLM in Organizations: Address data and IP security challenges, ensuring safe AI usage