By Obinna Okechukwu
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
- 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
- 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
- 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
- Core strengths of LLMs
- Common failure modes (hallucinations, math, real-time info)
- Safeguards and best practices
- Building a safe assistant class
- Example: SmartSafeAssistant
- 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
- 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
- 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
- Retry strategies and exponential backoff
- Rate limiting and quotas
- Streaming vs batch responses
- Caching strategies
- Multi-provider failover
- Example: Production-grade IoT command processor
- Anatomy of effective prompts
- Zero-shot, few-shot, and chain-of-thought prompting
- Role-based prompting
- A/B testing prompts
- Practical IoT diagnostic system
- Self-consistency
- Tree-of-thought prompting
- ReAct (Reasoning and Acting)
- Constitutional AI
- Prompt chaining and workflows
- JSON mode and structured outputs
- Pydantic integration and schema validation
- Constrained code generation
- Template-based code generation
- Example: IoT configuration generator
- Technical documentation generation
- Domain-specific code generation and review
- Data analysis and insights
- Customer service and troubleshooting guides
- Chatbots vs. agents
- The Perceive-Think-Act loop
- Agent components: perception, memory, reasoning, action
- Building an autonomous IoT agent
- The two-step tool use loop
- Implementing function calling (OpenAI, Claude, Gemini)
- Tool registry and secure execution
- Orchestrating tool chains
- Security and permission management
- Agent frameworks (LangChain, AutoGen, CrewAI)
- State management and persistence
- Communication protocols
- Observability: logging, metrics, tracing
- Example: Industrial automation agent
- Multi-agent collaboration patterns
- Message passing and coordination
- Consensus mechanisms
- Hierarchical agent structures
- Example: Smart city IoT coordination system
- 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
- WebSocket integration
- Background task processing with Celery
- Caching strategies
- Rate limiting and queue management
- Example: Real-time IoT anomaly detection
- Monolith vs. microservices
- Event-driven architectures
- Database patterns (polyglot persistence)
- Configuration management
- Example: Scalable IoT analytics platform
- 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
- Token usage and cost structure
- Semantic caching
- Model selection strategies
- Batch processing
- Cost monitoring and ROI
- Example: Cost-effective IoT analysis
- API key management and secret storage
- Prompt injection and input sanitization
- Output filtering and moderation
- Audit logging and compliance
- Example: Secure healthcare IoT system
- Structured logging
- Metrics and KPIs
- Distributed tracing
- Real-time monitoring dashboards
- Example: IoT system health dashboard
- Unit and integration testing for AI
- Regression testing with golden datasets
- Load testing AI endpoints
- Example: IoT command validation testing
- CI/CD for AI applications
- Environment management
- Blue-green deployments
- Feature flags for AI features
- Rollback strategies
- Example: IoT firmware update system
- 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
- RAG workflow: indexing, retrieval, generation
- Vector databases and embeddings
- Building a RAG-powered assistant
- RAG vs. fine-tuning
- Example: IoT documentation assistant
- From chains to workflows (DAGs)
- Building a workflow orchestrator
- Error handling and retries
- Human-in-the-loop patterns
- Example: Automated IoT incident response
- True multimodal AI
- Edge AI deployment
- Federated learning
- Quantum computing and AI
- Example: Next-gen IoT architectures
- From technology to solution: product thinking
- The Lean AI Canvas
- MVPs and data flywheels
- Ethical review and safety
- Launching and iterating AI products
- 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.
- 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.
- 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.
- 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.
- 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.
- Latent.Space Newsletter – Weekly deep dives and news for AI engineers.
- AI Fire Academy – Practical guides, workflows, and a community for mastering AI engineering.