2026.01.16
Chapter 1: Foundations of Applied AI: Python & System Thinking
Learn the fundamentals of Applied AI with Python, setting up your environment, and cultivating system thinking.
ACCESS >>// A COMPREHENSIVE GUIDE TO TEACH ME A COMPLETE STEP-BY-STEP CAREER PATH FOR APPLIED AI AND AGENTIC AI DEVELOPMENT, STARTING FROM FOUNDATIONAL PROGRAMMING AND SYSTEM THINKING, THEN MOVING INTO WORKING WITH LARGE LANGUAGE MODELS AND AI APIS, PROMPT ENGINEERING, TOOL USE, FUNCTION CALLING, RETRIEVAL-AUGMENTED GENERATION (RAG), MEMORY AND STATE MANAGEMENT, AGENT ORCHESTRATION, MULTI-AGENT SYSTEMS, AI-DRIVEN WORKFLOWS, EVALUATION AND OBSERVABILITY, COST AND LATENCY OPTIMIZATION, SECURITY AND PRIVACY CONSIDERATIONS, AND PRODUCTION DEPLOYMENT, WITH A STRONG FOCUS ON BUILDING REAL APPLICATIONS THAT USE AI AT ITS FULL POTENTIAL, INCLUDING PROGRESSIVELY CHALLENGING HANDS-ON PROJECTS, DAILY PRACTICE IDEAS, SYSTEM DESIGN PATTERNS, COMMON FAILURE MODES, AND SECTIONS THAT ENCOURAGE INDEPENDENT EXPERIMENTATION AND IDEA GENERATION SO I CAN GROW FROM BEGINNER TO PROFESSIONAL APPLIED AI ENGINEER AND PRODUCT BUILDER, ALIGNED WITH MODERN AGENTIC AI PRACTICES AS OF JANUARY 2026. CHAPTERS
2026.01.16
Chapter 2: Understanding Large Language Models (LLMs) & AI APIs
Learn how to interact with Large Language Models using AI APIs in Python, setting the foundation for building intelligent applications.
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Chapter 3: Mastering Prompt Engineering: The Art of Instruction
Learn the principles and practices of crafting effective prompts to guide Large Language Models (LLMs) for specific tasks.
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Chapter 4: Tool Use & Function Calling: Extending LLM Capabilities
Learn how to extend LLM capabilities with tools and function calling for real-world applications.
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Chapter 5: Retrieval-Augmented Generation (RAG): Beyond Model Knowledge
Learn how to implement a Retrieval-Augmented Generation (RAG) system to enhance LLMs with external knowledge.
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Chapter 6: Memory & State Management for Persistent AI Interactions
Learn how to implement memory and state management in AI applications using LangChain, Vector Stores, and RAG.
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Chapter 7: Introduction to AI Agents: Autonomy in Action
Learn to build and understand AI agents that perceive, reason, and act autonomously using Python and LLMs.
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Chapter 8: Agent Orchestration & Multi-Agent Systems
Learn how to design, build, and coordinate multiple AI agents for complex tasks using agent orchestration.
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Chapter 9: Designing AI-Driven Workflows & Complex Agent Patterns
Learn how to design and orchestrate complex AI-driven workflows using multi-agent systems.
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Chapter 10: Evaluation, Observability & Debugging AI Agents
Learn how to evaluate, observe, and debug AI agents for better performance and reliability.
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Chapter 11: Cost, Latency & Optimization for AI Solutions
Learn to optimize the cost and latency of your AI and agentic solutions, exploring techniques for token management, model selection, caching, and …
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Chapter 12: Security, Privacy & Ethical AI Development
Learn about the unique security threats, privacy concerns, and ethical considerations in developing agentic AI systems using LLMs.
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Chapter 13: Production Deployment & Scaling AI Agents
Learn how to deploy and scale AI agents in production using Docker and Kubernetes.
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Chapter 14: Hands-On Project: Building a Smart Research Assistant Agent
Learn to build a Smart Research Assistant Agent using agentic AI principles and tools.
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Chapter 15: Hands-On Project: Developing an Autonomous Workflow Agent
Learn to build an autonomous workflow agent using Python and LLM APIs.
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Chapter 16: Hands-On Project: Building a Collaborative Multi-Agent System
Learn to build a collaborative multi-agent system using Python and LLMs, enhancing problem-solving through collective intelligence.
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