The AI Engineer’s Handbook

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.

Ref - AI Engineer - Next Big Tech Role

What is AI Engineering ?

AI Engineering involves crafting and deploying AI systems by leveraging pre-trained models and existing AI tools to address practical challenges. AI Engineers prioritize the application of AI in real-world contexts, enhancing user experiences, and automating processes, rather than creating new models from the ground up. Their efforts are directed towards making AI systems efficient, scalable, and easily integrable into business applications, setting their role apart from AI Researchers and ML Engineers, who focus more on developing new models or advancing AI theory.

Core Concepts & Principles

Who is it for ?

• 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.

Essential AI & LLM Vocabulary

A comprehensive glossary of key terms in AI Engineering, Large Language Models (LLMs), and Machine Learning.

Core LLM Concepts

• Foundation Model - Large Language Model (LLM) designed to generate and understand human-like text across diverse applications and use-cases
• Transformer Architecture - Revolutionary neural network design known for its attention mechanism and parallel processing capabilities in natural language processing
• Prompt Engineering - Art and science of crafting effective inputs to LLMs to generate accurate, relevant, and desired outputs
• Context Window - Maximum number of input tokens (words/characters) an LLM can process when generating responses
• Zero-Shot Learning - LLM’s ability to perform tasks without specific examples, using pre-existing knowledge
• Few-Shot Learning - Technique of providing minimal examples to guide LLM task performance

RAG & Knowledge Management

• RAG (Retrieval-Augmented Generation) - Advanced technique combining knowledge retrieval with LLM generation for enhanced accuracy
• Vector Database - Specialized database storing numerical representations of text for efficient similarity search
• Embedding Models - Neural networks converting text into mathematical vectors for processing
• Knowledge Base (KB) - Structured collection of information used to augment LLM responses
• Chunking Strategies - Methods for breaking down documents into optimal sizes for processing
• Vector Search - Algorithms finding relevant information based on semantic similarity

AI Agents & Automation

• LLM Agents - Autonomous systems combining LLMs with planning and memory capabilities
• Function Calling - LLM capability to interact with external tools and APIs
• Agent Memory Systems - Components storing and managing agent interaction history
• Planning Modules - Systems for breaking complex tasks into manageable steps

Security & Ethics in AI

• Prompt Injection - Security vulnerability where malicious inputs manipulate LLM behavior
• AI Bias - Systematic prejudices in AI systems requiring careful mitigation
• Responsible AI Development - Framework ensuring ethical, fair, and transparent AI systems
• AI Governance - Policies and practices regulating AI development and deployment
• Privacy-Preserving AI - Techniques protecting sensitive data in AI systems
• Model Robustness - AI system resilience against adversarial attacks and manipulation

Advanced Learning Paradigms

• Reinforcement Learning - Training method using reward-based feedback systems
• Federated Learning - Distributed training preserving data privacy
• Multi-task Learning - Training models to excel at multiple related tasks
• Continual Learning - Ongoing model adaptation without forgetting previous knowledge

Enterprise AI Implementation

• LLMOps - Operational practices for managing LLM deployments
• AI Compliance - Adherence to regulatory requirements in AI systems
• Model Monitoring - Tracking and maintaining AI system performance
• Red Team Testing - Security assessment through simulated attacks

Explore Complete AI & LLM Vocabulary Guide →

This vocabulary guide is regularly updated to reflect the latest developments in AI technology, ensuring developers and product leaders stay current with essential terminology and concepts.

AI - Mainframe

• 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