A path from a small idea to a product that works with context, interface, real data and tools.
For readers ready to move from learning to building and turn a real need into a prototype and lasting system.
By the end, you can define a product, build a prototype, maintain consistency and connect knowledge, APIs and agents.
Start with a small need, a clear user and a definition you can test.
This chapter starts withStart with the ability to explain a need, understand system components and think about the user before choosing a tool.
You don't need to know how to code to work with AI — but you do need to develop three core skills: explaining clearly what you want, understanding how systems work, and thinking like a product manager.
Why nowUse that mindset to choose a small, concrete problem instead of starting with a large platform.
In an era where anyone can build their own digital solution in minutes, disposable apps are becoming a tool for learning, problem-solving, and smart self-customization with the help of AI.
Why go deeper hereBefore a full product document, practice stating a goal, context, constraints and an outcome an agent can understand.
What is the RICE Framework and how to use it to write more precise prompts that return useful, consistent answers.
Why nowThe brief becomes a product definition: who it is for, why it matters, what is included and how success is measured.
A PRD defines what you're building, for whom, and why. When working with AI agents, it becomes a critical part of context engineering — ensuring precision, consistency, and efficiency throughout the development process.
Move from a definition to a working product through different examples.
This chapter starts withUse the PRD to build a first version through conversation with AI and short iterations.
Discover how to build applications with AI-assisted tools like Replit, Claude, and Lovable. A practical guide with tool comparisons, implementation ideas, a hands-on project, and tips for getting started with Vibe Coding.
Why go deeper hereAfter the foundation, examine a complete content product connecting information, summarization, voice and interface without code.
In this guide, we'll build a voice summary app for AI news, step by step — using GPT, Lovable, and ElevenLabs. No coding required.
Why go deeper hereMove from a content product to an interactive assistant that accepts user input and creates an adapted process.
A guide to building a no-code AI job interview assistant, including process structure, tools, and how tailored interview questions are generated.
Why go deeper hereAn assistant that returns an answer becomes a simulation where the user practices, receives feedback and learns through action.
How to build an AI-powered customer service simulator with Amazon PartyRock, and what organizations can learn from it about employee training.
Why go deeper hereAfter a simulation, examine a complete experience around a real user moment: joining an organization before the first day.
How to use Vibe Coding to improve the pre-boarding experience and build solutions that connect employees even before their first day.
Why go deeper hereThe final example expands the product idea: the same component-based process can also produce video and animation.
Creating videos and animations through code! Discover how to turn React components into professional video using Claude Code and Remotion.
Turn a prototype into a system that can keep evolving over time.
Why go deeper hereAfter several iterations, context accumulates. Tokens explain why information cannot be added without limit.
What tokens are in language models, how they affect the context window, and why they matter for working effectively with LLMs.
Why go deeper hereApply the token limit in a real building environment and remove context that does not serve the task.
The Claude Code context window is a precious resource. Before you've typed a single word, you've already burned thousands of tokens. This guide explains why that happens and how to deal with it.
Why nowAfter cleaning context, organize it in stable files that preserve decisions and instructions between sessions.
In this post we explain what MD files are, how they are used in AI workflows, and why defining them correctly matters when working with agents.
Why nowStable context preserves decisions; Storybook adds a source of truth for interface components and their states.
What Storybook is, why you should use it when building websites and applications with AI agents, how to install it, and what you can do with it.
Why go deeper hereAs the system grows, repeated instructions should not remain in a prompt. Package them as a Skill with process and checks.
If an AI agent is a new employee, a skill is their instruction sheet: what the goal is, what information matters, how to work, where to stop, and how to verify the result is good.
Now put it into practiceNow build a real Skill for a repeated product action and verify that another agent can use it.
A guided flow for creating Claude Agent Skills with SKILL.md preview, quality checks and ZIP export.
A Skill ready to review, download and share.
Now put it into practiceAfter building, compare it with reviewed examples to identify useful structures and improve your Skill.
A curated library of Claude Agent Skills reviewed before publication.
A ready-to-use Skill to study or adapt to your workflow.
Give the product information sources, APIs, tools and security boundaries.
Why go deeper hereAfter sources of truth for interface and instructions, add a source of truth for content through RAG.
What is a RAG Chatbot, how it connects a language model to a knowledge base, and why it improves answers, reliability, and user experience.
Why nowRAG enables reading knowledge. An API lets the product exchange data and take action in other systems.
What is an API, and how is it changing the rules of the game in artificial intelligence? A clear guide with examples, tools, and tips — from GPT to AI agents that work just like real employees.
Why nowAfter a point-to-point API connection, MCP offers a shared interface for agents to discover many tools and sources.
Meet MCP – a groundbreaking protocol that enables AI agents to access tools, files, and external systems in a smart, context-aware way, without the need for custom integrations. This isn't just another API – it's a new way of working with artificial intelligence.
Why go deeper hereAs more sources and tools are connected, risk also grows. Define boundaries before giving the system more power.
Two common security vulnerabilities in large language models, why they are dangerous, and how to think about safer LLM use in your organization.
Choose autonomy, divide responsibility and connect the system components.
Why go deeper hereWith data and tools available, decide which steps are fixed and which require an agent's judgment.
A clear explanation of the difference between standard automation and Agentic Workflow, and when to use each approach in your organization.
Why nowWhen a task is too large for one agent, divide it among specialists and define how they pass results.
Claude Code – an AI-powered development environment. What are its advantages, what are Subagents, and how I built an automated CMO agent that manages content across social media.
Why nowFinish with a complete system connecting a harness, context, memory, tools, permissions, orchestration and observability.
The difference between a chatbot and an autonomous AI agent, what an agent harness is, which components a real agent must have, and how Claude Code Agents, OpenClaw, and Hermes differ from one another.
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