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Compare AI for Developers Outlines

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AI for Developers Angular Data Science Laravel Python

AI for Developers - 3 Days

This focused 3-day program gives software developers a practical introduction to AI-assisted software engineering, including prompting, coding, debugging, testing, documentation, RAG, and workflow design.

Duration 3 Days
Delivery Depth
Best Fit Developers and teams who want a practical introduction to AI-assisted software engineering
Audience Software Developers Technical Leads QA Engineers IT Professionals transitioning into AI-assisted development
Prerequisites Programming experience in at least one language (Java, JavaScript, Python, PHP, etc.) Familiarity with software development lifecycle and source control practices Basic understanding of APIs, web applications, or modern development tools
Days 3
Main Topics 16

Included Topics

  • Introduction to AI-Assisted Software Engineering
  • AI Workspace and Core Concepts
  • Prompt Engineering
  • Code Generation and Refactoring
  • Building Reusable Skills
  • Debugging and Code Analysis
  • Test Case Generation
  • Documentation and Productivity
  • AI in Development Environments
  • Workflow Management
  • Advanced Prompting
  • Introduction to RAG
  • Multi-Agent Workflow
  • System Design with AI
  • Best Practices and Security
  • Capstone Project - AI-Assisted Development Pipeline
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AI for Developers - 5 Days

This 5-day accelerated program equips software developers and architects with practical knowledge to apply Generative AI, LLM APIs, RAG, prompt-driven development, and agentic AI patterns across the modern software delivery lifecycle.

Duration 5 Days
Delivery Depth
Best Fit
Audience Software Developers Software Architects Technical Leads QA Engineers IT professionals moving into AI-assisted software development
Prerequisites Programming experience (Java, JavaScript, Python, PHP, etc.) Basic understanding of APIs and web applications Familiarity with software development lifecycle
Days 5
Main Topics 27

Included Topics

  • Introduction to AI in Software Development
  • Fundamentals of Generative AI
  • Machine Learning Foundations
  • Understanding LLM Internals (Simplified)
  • Mathematical Intuition (High-Level)
  • Lab 1: Exploring AI Tools
  • Prompt Engineering Fundamentals
  • Vibe Coding (AI-Assisted Development Workflow)
  • AI Coding Tools and Platforms
  • Building a Prompt-Driven Development Workflow
  • Lab 2: AI-Powered Development
  • Working with LLM APIs
  • Embeddings and Vector Databases
  • Retrieval-Augmented Generation (RAG)
  • Building AI-Powered Applications
  • Lab 3: Build a RAG-Based Coding Assistant
  • Fine-Tuning and Customization
  • Working with Open Source LLMs
  • Introduction to Agentic AI
  • Multi-Agent Systems for Development
  • Lab 4: Build a Multi-Agent Coding Assistant
  • Enterprise AI Architecture
  • AI Security and Risk Management
  • Advanced RAG and Knowledge Systems
  • AI in Software Architecture
  • Final Capstone Project
  • Final Presentation and Evaluation
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