Introduction to AI Engineering (for software engineers)

By Obinna Okechukwu

What you learn

After completing this book, you will:

• Be able to implement production AI systems with confidence
• Have deep understanding of all major AI APIs
• Have a good understanding of common prompt engineering techniques
• Be able to design and deploy AI agents
• Be able to make informed architectural decisions
• Be able to work proficiently with multimodal AI applications

Book Structure

Part 1: Foundations (Chapters 1-8)

Chapter 0: Introduction

Chapter 1: Welcome to AI engineering

• What are LLMs and how do they work?
• The paradigm shift: describing vs. instructing
• Your first AI call in Python
• The structure of a conversation (roles: system, user, assistant)
• Practical example: IoT status interpreter
• A simple mental model: tokens, embeddings, prediction
• The landscape of AI models

Chapter 2: Core Concepts - Tokens, Embeddings, and Context

• Tokens and cost calculation
• Using tiktoken to count tokens
• Embeddings and semantic search
• Context windows and memory limitations
• Practical example: semantic search for IoT troubleshooting
• Managing conversation history

Chapter 3: Setting Up Your Development Environment

• Python environment setup
• Essential libraries: openai, python-dotenv
• API key security and secret management
• Project structure and virtual environments
• Building a command-line AI chatbot
• Creating requirements.txt

Chapter 4: Understanding AI Capabilities and Limitations

• Core strengths of LLMs
• Common failure modes (hallucinations, math, real-time info)
• Safeguards and best practices
• Building a safe assistant class
• Example: SmartSafeAssistant

Part 2: API Mastery (Chapters 5-12)

Chapter 5: OpenAI API Complete Guide

• Making your first API call
• System messages and personality
• Managing conversation history
• Controlling creativity (temperature, max_tokens)
• Streaming responses
• Function calling
• Vision and audio capabilities
• Example: E-commerce recommendation assistant

Chapter 6: Anthropic Claude API Mastery

• Claude API basics and differences from OpenAI
• System prompts and persona control
• Long-context analysis and document Q&A
• Vision and tool use
• Example: IoT fleet management with visual diagnostics
• Model comparison and selection

Chapter 7: Google AI (Gemini) API Guide

• Gemini API setup and multimodal capabilities
• Video, audio, and PDF analysis
• Function calling and tool integration
• Building a predictive maintenance system
• Model comparison and integration with Google services

Chapter 8: API Design Patterns and Best Practices

• Retry strategies and exponential backoff
• Rate limiting and quotas
• Streaming vs batch responses
• Caching strategies
• Multi-provider failover
• Example: Production-grade IoT command processor

Part 3: Prompt Engineering Mastery (Chapters 9-14)

Chapter 9: Fundamental Prompt Engineering

• Anatomy of effective prompts
• Zero-shot, few-shot, and chain-of-thought prompting
• Role-based prompting
• A/B testing prompts
• Practical IoT diagnostic system

Chapter 10: Advanced Prompting Strategies

• Self-consistency for improved reliability
• Tree-of-thought prompting for complex reasoning
• ReAct (Reasoning and Acting) for dynamic problem solving
• Multi-Agent Debate for reducing bias and improving robustness
• Reflexion for iterative self-improvement
• Prompt chaining and workflows

Chapter 11: Structured Output Generation

• JSON mode and structured outputs
• Pydantic integration and schema validation
• Constrained code generation
• Template-based code generation
• Example: IoT configuration generator

Chapter 12: Domain-Specific Prompting

• Technical documentation generation
• Domain-specific code generation and review
• Data analysis and insights
• Customer service and troubleshooting guides

Part 4: Building AI Agents (Chapters 13-18)

Chapter 13: Introduction to AI Agents

• Chatbots vs. agents
• The Perceive-Think-Act loop
• Agent components: perception, memory, reasoning, action
• Building an autonomous IoT agent

Chapter 14: Tool Use and Function Calling

• The two-step tool use loop
• Implementing function calling (OpenAI, Claude, Gemini)
• Tool registry and secure execution
• Orchestrating tool chains
• Security and permission management

Chapter 15: Building Production Agents

• Agent frameworks (LangChain, AutoGen, CrewAI)
• State management and persistence
• Communication protocols
• Observability: logging, metrics, tracing
• Example: Industrial automation agent

Chapter 16: Multi-Agent Systems

• Multi-agent collaboration patterns
• Message passing and coordination
• Consensus mechanisms
• Hierarchical agent structures
• Example: Smart city IoT coordination system

Part 5: Python Web Applications (Chapters 17-19)

Chapter 17: Building AI-Powered Web Applications with Python

• Flask and FastAPI for AI endpoints
• File uploads (images, audio, PDFs)
• Streaming responses and SSE
• WebSockets for real-time updates
• Example: IoT device management dashboard

Chapter 18: Real-Time AI Applications

• WebSocket integration
• Background task processing with Celery
• Caching strategies
• Rate limiting and queue management
• Example: Real-time IoT anomaly detection

Chapter 19: AI Application Architecture Patterns

• Monolith vs. microservices
• Event-driven architectures
• Database patterns (polyglot persistence)
• Configuration management
• Example: Scalable IoT analytics platform

Part 6: Production Systems (Chapters 20-25)

Chapter 20: Scaling AI Applications

• Scaling challenges unique to AI
• Horizontal scaling and load balancing
• Queue-based architectures
• Database design for AI workloads
• Multi-level caching
• Example: Global IoT platform architecture

Chapter 21: Cost Optimization

• Token usage and cost structure
• Semantic caching
• Model selection strategies
• Batch processing
• Cost monitoring and ROI
• Example: Cost-effective IoT analysis

Chapter 22: Security and Safety

• API key management and secret storage
• Prompt injection and input sanitization
• Output filtering and moderation
• Audit logging and compliance
• Example: Secure healthcare IoT system

Chapter 23: Monitoring and Observability

• Structured logging
• Metrics and KPIs
• Distributed tracing
• Real-time monitoring dashboards
• Example: IoT system health dashboard

Chapter 24: Testing AI Systems

• Unit and integration testing for AI
• Regression testing with golden datasets
• Load testing AI endpoints
• Example: IoT command validation testing

Chapter 25: Deployment and DevOps

• CI/CD for AI applications
• Environment management
• Blue-green deployments
• Feature flags for AI features
• Rollback strategies
• Example: IoT firmware update system

Part 7: Advanced Topics (Chapters 26-30)

Chapter 26: Fine-Tuning and Custom Models

• When to fine-tune vs. prompt engineering or RAG
• Data preparation for fine-tuning
• Running a fine-tuning job
• Evaluating and deploying custom models
• Example: Specialized IoT assistant

Chapter 27: RAG (Retrieval Augmented Generation)

• RAG workflow: indexing, retrieval, generation
• Vector databases and embeddings
• Building a RAG-powered assistant
• RAG vs. fine-tuning
• Example: IoT documentation assistant

Chapter 28: AI Workflows and Orchestration

• From chains to workflows (DAGs)
• Building a workflow orchestrator
• Error handling and retries
• Human-in-the-loop patterns
• Example: Automated IoT incident response

Chapter 29: Emerging Patterns and Future Trends

• True multimodal AI
• Edge AI deployment
• Federated learning
• Quantum computing and AI
• Example: Next-gen IoT architectures

Chapter 30: Building Your AI Product

• From technology to solution: product thinking
• The Lean AI Canvas
• MVPs and data flywheels
• Ethical review and safety
• Launching and iterating AI products

Additional Resources

General AI Engineering & LLMs

• GenAI Handbook – A living, textbook-style roadmap for learning modern AI, LLMs, and generative models, with curated links to the best blogs, videos, and courses.
• The 2025 AI Engineering Reading List (Latent.Space) – A practical, annotated list of 50+ must-read papers, blogs, and models across LLMs, prompting, RAG, agents, codegen, vision, and more.
• Full End-to-End Pipeline for LLM Apps (Rohan's Bytes) – A 2024–2025 guide to building, deploying, and monitoring LLM-powered applications, with technical best practices and industry case studies.

Prompt Engineering

• Prompt Engineering Guide – Comprehensive guide to prompting techniques, from basics to advanced strategies like chain-of-thought, tree-of-thought, and multi-agent systems.
• OpenAI Prompt Engineering Guide – Official best practices and examples for crafting effective prompts.
• Prompt Engineering Mastery: The Complete Guide – A step-by-step roadmap for mastering prompt engineering and LLMs, with practical resources and project ideas.
• Anthropic Prompt Engineering Tutorial – Anthropic’s hands-on guide to prompt design for Claude models.

AI Agents & Autonomous Systems

• Awesome AI Agents (GitHub) – A massive, regularly updated list of 1,500+ resources, tools, frameworks, datasets, and courses for building and learning about AI agents.
• A Survey on LLM-based Autonomous Agents (arXiv) – Comprehensive academic survey of agent architectures, tool use, evaluation, and future directions.
• LangChain Documentation – The most popular open-source framework for building LLM-powered agents, tool use, and RAG systems.
• LlamaIndex Documentation – Framework for building data-augmented LLM applications and agentic workflows.

RAG (Retrieval-Augmented Generation)

• Deconstructing RAG (LangChain Blog) – Practical guide to RAG architectures, vector databases, and best practices.
• RAG Course (DeepLearning.AI) – Free video course on advanced RAG techniques and evaluation.

Benchmarks, Datasets, and Evaluation

• HELM: Holistic Evaluation of Language Models (Stanford) – A living benchmark for LLMs, covering knowledge, reasoning, safety, and more.
• Hugging Face Datasets – Thousands of open datasets for LLM training, fine-tuning, and evaluation.

Community & News

• Latent.Space Newsletter – Weekly deep dives and news for AI engineers.
• AI Fire Academy – Practical guides, workflows, and a community for mastering AI engineering.

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