1
Introduction to AI-Assisted Coding
What is prompt engineering and vibe coding, how LLMs generate code, strengths and limitations, human+AI collaboration, when not to trust AI.
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Learning objectives:
• Define prompt engineering and vibe coding
• Understand how LLMs generate code
• Know strengths, limitations, and when not to trust AI
• Apply human+AI collaboration model
2
Understanding Large Language Models
Tokens and context windows, temperature and sampling, system prompts, instruction following, hallucination, model limitations.
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Learning objectives:
• Understand tokens and context windows
• Use temperature and sampling effectively
• Apply system prompts and instruction following
• Recognize hallucination and model limitations
3
Prompt Engineering Fundamentals
Clear instruction writing, role-based and constraint-based prompting, output formatting, step-by-step prompting, iterative refinement.
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Learning objectives:
• Write clear instructions
• Use role-based and constraint-based prompting
• Control output formatting
• Apply step-by-step and iterative refinement
4
Structured Prompt Patterns
Zero-shot, few-shot, chain of thought, self-consistency, tree of thought, reflection prompting.
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Learning objectives:
• Apply zero-shot and few-shot prompting
• Use chain of thought, self-consistency, tree of thought
• Apply reflection prompting
5
Code Generation Prompts
Generating functions and full applications, clean code, documentation, tests, comments.
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Learning objectives:
• Generate functions and full applications
• Prompt for clean code, documentation, tests, comments
6
Debugging with AI
Explaining errors, step-by-step debug prompts, root cause analysis, refactoring, performance optimization, code review prompts.
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Learning objectives:
• Use AI to explain errors and debug step-by-step
• Apply root cause analysis and refactoring prompts
• Optimize performance and use code review prompts
7
Building Applications with AI
Breaking requirements into prompts, backend/frontend/database generation, API design, authentication setup.
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Learning objectives:
• Break requirements into prompts
• Generate backend, frontend, database schema
• Design APIs and set up authentication with AI
8
AI for System Design
Architecture diagrams, scalable systems, microservices design, system planning, deployment strategy prompts.
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Learning objectives:
• Generate architecture diagrams and design scalable systems
• Use AI for microservices and system planning
• Apply deployment strategy prompts
9
AI in Software Lifecycle
PRDs, test cases, CI/CD scripts, Docker files, documentation.
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Learning objectives:
• Write PRDs and generate test cases with AI
• Generate CI/CD scripts and Docker files
• Use AI for documentation
10
AI Pair Programming
Live code refinement, incremental building, versioning prompts, prompt templates, coding speed optimization.
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Learning objectives:
• Refine code live and build incrementally with AI
• Version prompts and use templates
• Optimize coding speed
11
Prompt Chaining
Multi-step prompt workflows, breaking large tasks, memory-based prompts, reusable prompt libraries.
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Learning objectives:
• Build multi-step prompt workflows
• Break large tasks and use memory-based prompts
• Maintain reusable prompt libraries
12
AI Agents for Development
Tool calling, function calling, automated code fixers, multi-agent development, auto testing agents.
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Learning objectives:
• Use tool and function calling
• Apply automated code fixers and multi-agent development
• Use auto testing agents
13
Full Stack Web App with AI
Project planning, backend API, frontend UI, database integration, deployment.
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Learning objectives:
• Plan and build full stack web app with AI
• Generate backend API, frontend UI, integrate database
• Deploy with AI assistance
14
AI SaaS App Development
Idea validation, feature planning, subscription system, logging and monitoring.
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Learning objectives:
• Validate ideas and plan features with AI
• Build subscription system
• Apply logging and monitoring
15
Automation Projects
Web scrapers, data analysis scripts, workflow automation, AI bots.
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Learning objectives:
• Build web scrapers and data analysis scripts with AI
• Implement workflow automation and AI bots
16
Security in AI Coding
Detecting vulnerabilities, secure code prompts, preventing injection attacks, code review for security.
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Learning objectives:
• Detect vulnerabilities with AI
• Use secure code prompts and prevent injection attacks
• Apply code review for security
17
Responsible AI Use
Avoiding over-reliance, verifying AI code, licensing and legal awareness, ethical AI development.
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Learning objectives:
• Avoid over-reliance and verify AI code
• Understand licensing and legal aspects
• Practice ethical AI development
18
AI Productivity Framework
Personal prompt library, prompt templates, reusable AI workflows, rapid prototyping strategy.
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Learning objectives:
• Build personal prompt library and templates
• Create reusable AI workflows
• Apply rapid prototyping strategy
19
AI for Interview Preparation
Solving DSA with AI, mock interview simulation, system design practice, code review practice.
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Learning objectives:
• Use AI for DSA and mock interviews
• Practice system design and code review with AI
20
Becoming a 10x AI Developer
Thinking in prompts, AI as thought partner, architecture-level prompting, scaling development speed.
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Learning objectives:
• Think in prompts and use AI as thought partner
• Apply architecture-level prompting
• Scale development speed