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AI Handbook - Learn Generative AI, LLMs, RAG and Agentic AI

Design, build, and ship production-ready AI systems and prep for AI interviews with structured practice.

Generative AI Handbook
Agentic AI Handbook
GenAI System Patterns

Generative AI

agentic ai

Agentic AI

Interview prep stream

Interview Prep

Fahim Avatar

Hi I’m Fahim

Over the years, I’ve watched software engineering evolve again and again. Frameworks change, platforms rise and fall, and expectations keep climbing. Lately, one shift has reshaped the day-to-day more than most: generative AI.

These systems make it easier than ever to build sophisticated features quickly. That speed is exciting, but it also creates new challenges. Teams can deploy powerful AI-driven behavior before they fully understand its limits, failure modes, or downstream impact.

I put this handbook together to help engineers slow that part down. It focuses on building intuition for generative and agentic AI, understanding how they behave in practice, and using them responsibly in production environments.

The handbook is free, and it’s meant to be practical. If it helps you exercise better judgment, build more reliable systems, and lead with clarity as AI becomes more common, it’s done its job.

Why building GenAI systems is a real engineering problem

Correctness is a spectrum

Correctness is a spectrum

There’s no test suite for “good enough.” Output quality is subjective, context-dependent, and shifts as models update.

Failures are invisible

Failures are invisible

Your system can be confidently wrong. Without robust evaluation, you won’t know it’s broken until users tell you.

actions have consequences

Actions have consequences

When AI can browse, execute code, or send emails, mistakes aren’t just bad answers: they’re bad outcomes.

Scale changes everything

Scale changes everything

Latency, cost, and reliability constraints force real tradeoffs between capability and practicality.

Learn what hiring teams are actually looking for

As AI becomes a core part of modern software, hiring expectations are shifting with it. Teams want engineers who can work across models, data, and tools, and who understand the tradeoffs involved in real production systems.

Learning how these components connect gives you a clearer picture of what interviews are really testing for today.

ship a working gen ai system

Generative AI: The Complete Guide

Agentic System Design

Agentic Design Patterns

Model Context Protocol

Start with the GenAI Guides

6-step roadmap: How to start

Large Language Models

Prompt Engineering

Retrieval Augmented Generation

Orchestration Frameworks

Fine-tuning LLMs

Agentic Frameworks

More free AI guides

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Frequently Asked Questions

Generative AI refers to AI systems capable of creating original content, such as text, images, audio, video, or code, by identifying patterns learned from existing data. Rather than merely analyzing or classifying information, generative AI produces unique outputs that resemble human-created work.
No. ChatGPT is an example of generative AI, but they are not synonymous. Generative AI is the broader category of technology, while ChatGPT is a specific application built using a large language model (llm) to generate text and maintain conversations.
AI is a vast field encompassing systems designed to analyze data, make predictions, or automate decisions, such as spam filters or recommendation engines. Generative AI is a specialized subset of AI focused specifically on the creation of new content, including writing text, generating images, or composing music.
Generative AI systems are commonly categorized by the type of content they produce:
  • Text generation (e.g., chatbots and content writing tools)
  • Image generation (e.g., creating visuals from text prompts)
  • Audio generation (e.g., speech, music, and sound effects)
  • Video generation (e.g., animated or AI-generated videos)
Generative AI certification is a formal credential that validates a learner’s proficiency in generative AI concepts, tools, and practical applications. These programs typically cover the foundations of generative models, prompt engineering, responsible AI implementation, and real-world use cases.
Generative AI platforms are software tools or services that enable users to build, deploy, or interact with generative models. These platforms offer various capabilities, including text and image generation, API access, model customization, and seamless integration into existing applications.
The generative ai Google course is a learning program provided by Google that introduces generative AI concepts, large language models, and practical applications. It is designed for both beginners and professionals seeking structured exposure to the generative AI field within Google’s toolset and ecosystem.
The AI Handbook is a free learning platform that helps you understand modern AI through structured, practical guides instead of scattered tutorials. It includes both a Generative AI Handbook, covering topics like LLMs, prompt engineering, RAG, vector databases, multimodal AI, and GenAI system design, and an Agentic AI Handbook, which focuses on AI agents, agentic RAG, design patterns, system design, MCP, A2A communication, and production-ready agentic systems. It is designed for software engineers, AI engineers, machine learning practitioners, students, and anyone who wants to build, deploy, or prepare for interviews involving real-world AI systems.
Yes. The AI Handbook is free to use and provides open access to its educational content, including both the Generative AI Handbook and the Agentic AI Handbook, along with additional guides, blogs, and AI news. While the site also highlights optional discounts on external AI courses, the handbook itself is available as a free learning resource for anyone looking to build practical knowledge in generative and agentic AI.

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