Debug School

rakesh kumar
rakesh kumar

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How Experienced Developers Can Learn AI and Agentic AI Faster Using ChatGPT, Claude and Real Projects

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Introduction

Explain the problem:

AI courses are often designed for beginners.
Long video playlists consume too much time.
Experienced developers already understand APIs, databases, authentication, queues, Docker and architecture.
They should focus on AI-specific concepts and practical implementation.

Why Video-First Learning Is Slow

Explain the limitations:

Long introductions
Repeated basic concepts
Passive learning
Outdated framework examples
No code review
No project-specific guidance
Difficult to find one exact answer in a long video

Clarify that videos are still useful for visual concepts, but they should not be the main learning method.

The Recommended Learning Formula 

Use this model:

Understand
   ↓
Build
   ↓
Break
   ↓
Debug
   ↓
Improve
   ↓
Test
   ↓
Explain
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Recommended time distribution:

20% Essential theory
60% Practical implementation
10% Debugging and testing
10% Documentation and revision
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How ChatGPT and Claude Reduce Learning Time

Explain how AI assistants can act as:

Personal tutor
Pair programmer
Architecture reviewer
Debugging assistant
Interviewer
Test-case generator
Documentation assistant

But also mention that developers must write and understand the code themselves.

The Wrong Way to Use AI

Show poor prompts:

Explain LangGraph.
Teach me RAG.
Build an AI agent.
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These prompts usually generate generic theory or an oversized project.

The Correct Prompt Pattern

Use:

I am an experienced Python and Laravel developer.

Teach me [TOPIC] using a build-first approach.

Follow this sequence:

1. Explain only the essential theory required to start.
2. Show the smallest runnable example.
3. Give me a mini-project to implement myself.
4. Do not give the complete solution.
5. Review my implementation when I share it.
6. Create realistic failures for me to debug.
7. Upgrade the working version toward production.
8. Test me using practical and interview questions.

Compare unfamiliar AI concepts with APIs, Laravel services, queues,
events, middleware, state machines and microservices.

Include validation, security, retries, timeouts, logging, testing,
cost control and deployment.
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Topic-by-Topic Practical Prompt Strategy

Cover the following core topics.

LLM fundamentals

Project:

Build an LLM experiment lab for tokens, temperature,
structured output, embeddings and hallucination.
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FastAPI AI services

Project:

Build a FastAPI AI microservice and integrate it with Laravel.
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Prompt engineering

Project:


Build and test versioned prompts for a real content-generation API.
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Structured output and tool calling

Project:

Build an assistant that returns validated Pydantic output
and calls approved application tools.
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Embeddings

Project:


Build semantic search using PostgreSQL and pgvector.
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RAG

Project:

Build a knowledge assistant that answers only from retrieved documents
and returns citations.
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Agentic AI

Project:

Build a bounded tool-using agent in plain Python.
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LangGraph

Project:

Convert the plain Python agent into a resumable,
checkpointed LangGraph workflow.
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MCP

Project:

Build a secure MCP server exposing limited business tools.
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Multi-agent systems

Project:

Build and compare a single-call, single-agent and multi-agent workflow.
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Evaluation and security

Project:

Create a golden dataset, regression tests, security tests
and observability for the AI applications.
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Production deployment

Project:

Deploy Laravel, FastAPI, LangGraph workers, Redis,
PostgreSQL and an LLM provider using Docker.
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A Practical One-Hour Learning Session 

0–10 minutes
Understand essential concepts

10–20 minutes
Study architecture and minimal example

20–40 minutes
Implement the feature yourself

40–50 minutes
Debug one failure and add tests

50–60 minutes
Review, explain and answer questions
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When to Use Documentation, Videos and AI

Recommended order: 

ChatGPT/Claude explanation
        ↓
Official quickstart
        ↓
Build the project
        ↓
Watch a short video only when stuck
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Real Projects for Experienced Developers

Use existing business products rather than generic chatbot projects.

MotoShare

Vehicle description generator
Rental support assistant
Vehicle availability agent
Pricing recommendation system
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HolidayLandmark

AI itinerary generator
Trip creation assistant
Travel-document RAG assistant
Organizer support agent
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DevOpsSchool

Documentation knowledge assistant
Course recommendation system
Production error investigation agent
Test-case generation workflow
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MyHospitalNow

Hospital quote explanation assistant
Medical-document information retrieval
Hospital discovery assistant
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For healthcare projects, clearly separate informational assistance from medical diagnosis.

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