About Handbook

The Developer’s Handbook

AI Engineering, Product Leadership, and System Design

A comprehensive guide for developers, product leaders, and system architects aiming to leverage AI technologies effectively. It focuses on bridging technical knowledge with strategic insights, ensuring that readers can build scalable, impactful solutions.

AI Engineering

Large Language Models (LLMs)

  • Understanding foundation models and transformers
  • Model selection and evaluation
  • Fine-tuning and adaptation strategies
  • Deployment and scaling considerations
  • Performance optimization techniques

Vector Databases & Embeddings

  • Vector database architecture and selection
  • Embedding models and generation
  • Similarity search and retrieval
  • Indexing and optimization strategies
  • Scaling vector operations

Prompt Engineering & Interaction

  • Crafting effective prompts for specific tasks
  • Implementing few-shot and zero-shot learning
  • Managing context and token limitations
  • Optimizing prompt strategies for different use cases
  • Handling prompt injection and security concerns

RAG Systems & Knowledge Management

  • Building Retrieval-Augmented Generation systems
  • Managing knowledge bases and vector databases
  • Implementing efficient chunking and indexing strategies

AI Agents & Automation

  • Developing LLM-powered autonomous agents
  • Implementing memory and planning systems
  • Integrating function calling and external tools

Ethics & Governance

  • Ensuring responsible AI development
  • Managing bias and alignment
  • Implementing privacy-preserving techniques
  • Maintaining compliance with regulations

Security & Robustness

  • Protecting against adversarial attacks
  • Implementing security measures
  • Ensuring model robustness
  • Conducting red-team assessments

AI Product Leadership

  • Understanding AI’s role in product innovation and customer experience
  • Frameworks for evaluating AI feasibility and ROI in business contexts
  • Managing cross-functional teams working on AI projects
  • Ethical considerations and compliance in AI product development
  • Future trends in AI and their potential business impacts

AI Infrastructure

Mainframe Systems

  • Exploring the integration of AI with legacy mainframe systems
  • Leveraging AI for enhanced mainframe automation and optimization
  • Tools and platforms that enable AI integration in mainframe environments
  • Case studies on AI-driven improvements in mainframe performance and efficiency
  • Challenges and solutions when applying AI to traditional mainframe systems

System Design

  • Principles of designing scalable and fault-tolerant systems
  • Deep dives into architecture patterns: microservices, event-driven systems, and serverless computing
  • AI-specific architectures, such as data pipelines and model-serving strategies
  • Hands-on examples and exercises on system design problem-solving
  • Tools for system visualization, monitoring, and optimization

Target Audience

  • Software Engineers aiming to expand their expertise in AI and system design.
  • Product Leaders looking to understand AI’s impact on product strategy.
  • Architects focusing on designing systems that integrate AI and scale efficiently.
  • Mainframe Engineers exploring AI solutions to modernize legacy systems.

Learning Outcomes

Readers will gain a robust understanding of AI applications, leadership strategies for AI-driven products, the foundations of scalable system design, and the ways AI can enhance traditional mainframe systems, enabling them to create impactful, future-ready solutions.

👋 Introduction