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Generative AI for Software Engineers & Developers

Name: Generative AI for Software Engineers & Developers
Rating: 4.2 (3376 reviews)

1000+ AI Prompts + 20 AI Agent Projects: ChatGPT, Claude, Gemini, Copilot, LangGraph, RAG & MCP

Created byEdcorner Learning -190000+ Students, 4+ Star Ratings

Last updated 9/2026

English

English [Auto],Spanish [Auto],

What you'll learn

Understand the fundamentals of Generative AI, including Transformers, Diffusion Models, and their relevance to software engineering.
Access a curated 1000+ expert prompts tailored to accelerate software engineering tasks across coding, testing, DevOps, architecture, and security.
Differentiate clearly between Predictive AI and Generative AI in the context of software development workflows.
Explore real-world use cases of GenAI for code generation, bug fixing, documentation, DevOps automation, and architecture design.
Master Prompt Engineering techniques: Zero-shot, Few-shot, Chain of Thought (CoT), Tree of Thought (ToT), and reusable prompt templates.
Generate high-level software architectures, including ER diagrams, sequence diagrams, and make architectural trade-off analyses using GenAI.
Auto-generate multi-file codebases, classes, modules, and functions while adhering to SOLID and DRY principles.
Perform code refactoring, enhance readability, optimize performance, and add professional-grade documentation using AI assistance.
Automate static code analysis, bug detection, anti-pattern recognition, and pull request reviews via Generative AI prompts.
Learn how to generate Unit Tests, Integration Tests, E2E Tests, API Tests, Fuzz Tests, and achieve better code coverage.
Build Dockerfiles, Kubernetes manifests, Terraform scripts, and automate GitHub Actions/GitLab CI/CD pipelines using GenAI.
Design robust Infrastructure as Code (IaC) systems and automate monitoring setups with Prometheus and Grafana using prompt-driven workflows.
Define and monitor Service Level Objectives (SLOs) and Service Level Indicators (SLIs) to maintain operational excellence.
Create automated runbooks and disaster recovery playbooks driven by AI to boost reliability engineering practices.
Implement Secure Code Generation, threat modeling, vulnerability detection, and automate SOC2, HIPAA, GDPR compliance drafts.
Apply AI-based tools for Static Application Security Testing (SAST) and Dynamic Application Security Testing (DAST).
1000+ AI Native Software Engineering Prompts
20 Practical AI Agent Projects for Software Developers

Course content

13 sections • 167 lectures • 7h 54m total length

What Is Generative AI? Software Engineering & Developer Use Cases2:22
Generative AI creates original text, images, code, audio, or video using transformers and self-attention. Accelerate software development with tools like Copilot and chat assistants, while managing hallucinations and ethical concerns.
How Generative AI and Transformer Models Work2:22
Unlock how transformer models and diffusion models power generative ai, enabling long-context text and code writing, protein folding predictions, and image, audio, and video generation through self-attention and denoising.
Generative AI vs Predictive AI for Software Development2:12
Explore how predictive ai analyzes data to forecast outcomes and how generative ai creates new code, tests, and docs, showing how they complement each other in software development.
Generative AI for Coding, Debugging, Testing & Documentation2:35
Generative AI accelerates software development by enabling code generation, bug fixing, and automated documentation, while supporting DevOps tasks and architecture design with tools like GitHub Copilot, Amazon CodeWhisperer, and Replit.
ChatGPT, Claude, Gemini, Microsoft Copilot & AI Coding Assistants2:35
Explore how GPT four, Claude three, Codex, Gemini 1.5, and Code Llama empower software engineers with code generation, debugging, architecture decisions, documentation, and multi-modal design insights.

Architecture of LLMs: Transformer Networks, Self-Attention Mechanism2:24
Explore how transformer networks and self-attention power large language models like GPT-4 and Claude, enabling parallel processing, long sequences, multi-head attention, and tasks from coding to translation.
Tokenization, context window, and limitations2:24
Explore tokenization, context windows, and their limits in large language models like GPT-4 and Claude, and understand how memory, windowing, and retrieval affect performance and cost.
Fine-tuning vs In-context Learning2:10
Compare fine tuning and in context learning for llms GPT-4, Claude 3, Code Llama. Specialize models with fine tuning; in context learning adapts tasks while the base model remains unchanged.
Specialized Code LLMs: Codex, StarCoder, CodeGen, AlphaCode2:25
Explore specialized code LLMs like Codex, StarCoder, CodeGen, and AlphaCode that enhance coding, debugging, and software automation. Learn multilingual code generation, translation, optimization, and competitive programming capabilities.
Introduction to model APIs (OpenAI, Hugging Face, Anthropic)2:35
Explore how OpenAI, Hugging Face, and Anthropic provide model APIs to access large language models like GPT-4 and Claude, enabling integration via requests and features like temperature and function calling.

Principles of Effective Prompt Design2:14
Master principled prompt design for generative AI, using clear prompts and context setting to achieve precise outputs. Employ instructional sequencing, format specification, and iterative refinement for reliable results.
Zero-shot, One-shot, Few-shot prompting2:17
Explore zero shot, one shot, and few shot prompting in generative AI, guiding models like GPT four, Claude three, Gemini 1.5, and Code Llama toward accurate, aligned outputs.
Chain of Thought (CoT) and Tree of Thought (ToT) prompting2:17
Examine chain of thought and tree of thought prompting to boost reasoning, problem solving, and decision making in AI models like GPT four, Claude three, and Gemini 1.5.
Creating reusable prompt templates2:23
Create reusable prompt templates to scale generative AI across tasks, standardize inputs and outputs, automate AI interactions, and boost software engineering productivity.
Auto-suggesting patterns, snippets, or microservices architectures2:22
Use generative AI to auto-suggest design patterns, code snippets, and microservices architectures from natural language prompts and prompting techniques, accelerating prototyping while aligning with industry best practices.

High-level architecture generation from prompts2:33
Empower software engineers to generate scalable high-level architectures from prompts using generative AI, covering front end to databases, authentication, payment integration, and microservices.
Microservices vs Monolithic: AI design recommendations2:32
Explore AI-driven recommendations for choosing between microservices and monolithic architectures, considering scalability, team structure, deployment, and regulatory concerns; learn when to start with modular monoliths and evolve to microservices.
Generating ER diagrams, sequence diagrams, component diagrams2:25
Use generative AI to quickly generate ER diagrams, sequence diagrams, and component diagrams from well-framed prompts, producing detailed schemas and diagram code (plantuml) that save time and standardize documentation.
Architectural trade-off analysis by GenAI2:34
Generative AI guides architectural trade-off analysis by evaluating scalability, performance, cost, security, and complexity across monolith vs microservices, PostgreSQL vs MongoDB, and Lambda vs Kubernetes.
Technology Stack evaluation prompts2:38
Leverage AI-driven prompts to evaluate technology stacks, selecting languages, frameworks, databases, cloud services, and DevOps tools with clear reasoning for scalable, secure, and maintainable systems.

Multi-file code generation and folder structures2:38
Discover multi-file code generation and folder structure planning that lets generative ai create complete project scaffolds for full stack apps, from nodejs rest api back ends to react front ends.
Auto-generation of classes, modules, functions2:26
Generative AI models auto generate classes, modules, and functions from prompts. Learn to build Python libraries and Node.js modules with MongoDB, including email validators with input validation and error handling.
Code refactoring: improving readability, performance, and maintainability2:26
Generative AI models such as GPT four, Claude three, and Code Llama guide code refactoring to improve readability, performance, and maintainability through precise prompts that preserve functionality.
Adhering to SOLID principles, DRY principles2:22
Generative ai helps software engineers enforce solid and dry principles during code generation and refactoring, applying single responsibility, dependency inversion, and interface segregation for modular, maintainable architectures.
Adding comments and documentation with GenAI2:33
Harness generative AI models like GPT-4, Claude 3, and Code Llama to automatically add comments and generate documentation for code bases, improving collaboration, onboarding, and software health.

Prompting GenAI to review PRs (Pull Requests)2:20
Leverage genai models to review pull requests with prompts, delivering first-pass checks on code quality, architecture, security; identify long methods, unclear names, missing error handling, and vulnerabilities before human review.
Static Code Analysis with GenAI (security, performance, maintainability)2:27
leverage generative AI for static code analysis to improve security, performance, and maintainability through prompt-based techniques that scan code for vulnerabilities and inefficiencies.
Bug detection, anti-pattern recognition2:25
Leverage generative AI to detect bugs and anti-patterns across Python, Java, and JavaScript code, using smart prompts to identify logical errors and maintainability risks for proactive quality.
Auto-suggesting improvements and refactorings2:27
Auto-suggest improvements and refactorings across Python, Java, and Node.js codebases to boost readability, performance, and maintainability through precise prompts and targeted optimizations.

Generating Unit Tests, Integration Tests, and E2E Tests2:26
Leverage Generative AI to automatically generate unit tests, integration tests, and E2E tests with well-structured prompts for comprehensive coverage.
Writing test cases from function signatures2:21
Explore how generative AI writes unit tests from function signatures, producing positive, edge, and invalid test cases across Python, Node.js, and Java, with TDD-driven prompts and assertions.
Creating API tests (example: Postman collections via AI)2:19
Automate api test creation with generative ai models to generate postman collections from api descriptions or open api specs, creating comprehensive test suites that validate functionality, error handling, and performance.
Building fuzz tests and boundary tests using AI2:28
Leverage generative AI models to build fuzz and boundary tests, uncovering hidden bugs and security vulnerabilities in APIs, input validation, file uploads, and database constraints.
Code coverage analysis with GenAI-generated tests2:22
Leverage GenAI-generated tests to improve code coverage across functions, branches, and conditions. Use well-designed prompts to generate targeted tests that reveal untested paths and strengthen reliability.

Generating Dockerfiles, Kubernetes manifests, Terraform scripts2:22
Leverage generative AI to auto-generate dockerfiles, Kubernetes manifests, and Terraform scripts from natural language prompts for rapid, scalable infrastructure crafted for software engineers and developers.
Automating GitHub Actions, GitLab CI/CD pipelines2:25
Generative ai models automate creating GitHub actions and GitLab ci/cd pipelines, streamlining build, test, and deployment processes by using prompts to generate full yaml configurations with best practices.
Infrastructure as Code (IaC) prompts2:29
Leverage generative AI to automate infrastructure as code with Terraform, Pulumi, and AWS CDK, delivering modular, secure cloud configurations. Enable reusable modules and automated DevOps workflows.
Monitoring setup: Prometheus, Grafana dashboards using prompts2:29
Automate Prometheus and Grafana monitoring with prompts to generate Kubernetes scrape configs, node exporters, kube-state-metrics, alert rules, and Grafana dashboards, including Loki log aggregation.

Generating Service Level Objectives (SLOs) and Service Level Indicators (SLIs)2:38
Generative AI enables auto generation of SLOs and SLIs for REST APIs and SaaS apps, defining measurable targets for availability, latency, and error rate to strengthen SRE practices.
Designing Observability dashboards2:28
Leverage generative AI to design observability dashboards for cloud native systems, microservices, and enterprise apps, using prompt based design to generate Grafana dashboards with Datadog and CloudWatch panels.
Creating automated runbooks for incidents2:36
Leverage generative AI to generate automated runbooks for incidents, enabling DevOps and SRE teams to diagnose, remediate, escalate, and improve incident response with AI-driven prompts and dynamic, customized workflows.
Simulating failure modes and generating disaster recovery guides2:36
Generative AI models simulate failure modes and auto-generated disaster recovery guides for outages across Kubernetes, Azure PostgreSQL, and multi-region deployments, enabling cross-region failover, backup strategies, and resilient architecture.

Secure Code Generation practices2:23
Learn secure code generation with generative AI by crafting security focused prompts, validating outputs, and enforcing industry security standards through CI/CD scanners and secure defaults.
Threat modeling and vulnerability detection with prompts2:35
Leverage generative ai models like GPT four, Claude three, and Gemini 1.5 to perform threat modeling and vulnerability detection through strategic prompts, enabling early risk detection and secure devsecops maturity.
Compliance automation: SOC2, HIPAA, GDPR document drafts2:35
Generative AI accelerates compliance automation by drafting SOC 2, HIPAA, and GDPR documents, policies, and audit evidence, including privacy impact assessments, data processing agreements, and breach notifications.
AI in Static Application Security Testing (SAST) and Dynamic Application Securit2:23
Generative AI models revolutionize static and dynamic application security testing, enabling faster, context-aware vulnerability detection with SAST and DAST, AI-powered code reviews, and integrated security gates.

Requirements

Basic understanding of software development concepts
No prior experience with AI or Machine Learning is required
A curious mindset ready to explore Prompt Engineering, Generative AI APIs, and new coding techniques powered by AI tools.
Access to a laptop or computer with Internet connectivity

Description

Generative AI for Software Engineers & Developers

Generative AI for Software Engineers & Developers is a practical course designed for developers, software engineers, architects, DevOps professionals and technical professionals who want to apply modern artificial intelligence across the complete software development lifecycle.

You will explore how Generative AI, ChatGPT, Claude, Google Gemini, Microsoft Copilot, large language models (LLMs) and prompt engineering can support coding, debugging, software architecture, testing, DevOps, cloud engineering, application security, observability and AI-native software development.

The course progresses beyond basic AI-assisted coding into modern topics including Retrieval-Augmented Generation (RAG), vector databases, embeddings, LangChain, LangGraph, AI Agents, Agentic AI, multi-agent systems, Model Context Protocol (MCP), AI tool calling, LLM evaluation, prompt security, AI governance, responsible AI engineering and enterprise agentic architectures.

You will also receive access to 2000+ practical AI prompts across software engineering and AI-native development, together with 20 practical AI Agent and multi-agent projects covering real-world agentic software architectures.

Generative AI Foundations for Software Developers

Begin by understanding how Generative AI fits into modern software engineering.

You will explore:

Generative AI fundamentals
Generative AI vs Predictive AI
Transformer-based models
Software-engineering use cases
AI-assisted coding
Debugging
Testing
Documentation
Architecture support
Developer productivity

The course introduces ChatGPT, Claude, Google Gemini and Microsoft Copilot from a tool-flexible perspective so that you can apply the techniques using your preferred AI platform rather than becoming dependent on a single tool.

Large Language Models, Fine-Tuning and APIs

Develop a practical understanding of large language models (LLMs) and how they are used within software applications.

Topics include:

Transformer architecture
Self-attention
Tokenization
Context windows
LLM limitations
Fine-tuning
In-context learning
Specialized code models
Model APIs
AI application integration

You will understand how software developers can connect applications with modern AI models and evaluate when prompting, fine-tuning or API-based integration is appropriate.

Prompt Engineering for Coding and Software Development

Learn how to create effective prompts for technical and software-engineering workflows.

The course covers:

Prompt design principles
Zero-shot prompting
One-shot prompting
Few-shot prompting
Reusable prompt templates
Structured developer prompts
Prompt engineering for code generation
Prompt engineering for code review
Improving AI-generated technical outputs

Prompt engineering is treated as a practical software-engineering skill rather than simply a collection of copied prompts.

Generative AI for Software Architecture and System Design

Use Generative AI to support architectural analysis and system-design workflows.

Explore how AI can assist with:

High-level software architecture
Microservices architecture
Monolithic architecture
Architecture trade-off analysis
ER diagrams
Sequence diagrams
Technology-stack evaluation
Design patterns
Domain-Driven Design
Event-driven systems
Multi-tenant architectures
Legacy system modernization

The goal is not to allow AI to make architecture decisions automatically, but to use AI to accelerate analysis, alternatives and technical documentation.

AI Code Generation, Refactoring and Clean Code

Apply Generative AI to practical software-development activities including:

Multi-file code generation
Classes, modules and functions
Code refactoring
Clean Code
SOLID principles
DRY principles
Code documentation
Code explanation
Developer productivity

You will explore how AI can accelerate development while maintaining the need for testing, validation and engineering judgment.

Generative AI for Code Review and Static Analysis

Use AI to strengthen code-review workflows.

Topics include:

Pull Request review
Static code analysis
Bug detection
Anti-pattern identification
Refactoring recommendations
Code-quality improvement
Security-focused review

Generative AI can act as an engineering assistant, but generated recommendations should always be validated before implementation.

Generative AI for Software Testing and QA

Explore practical uses of AI in software testing, test automation and quality assurance.

You will work with concepts such as:

Unit-test generation
Integration testing
API testing
Test-case generation
Boundary testing
Fuzz testing
Code-coverage analysis
End-to-end testing
Load and stress testing

This helps connect Generative AI directly with modern software-quality workflows.

Generative AI for DevOps, CI/CD and Cloud Engineering

Apply Generative AI across DevOps and infrastructure workflows.

Topics include:

Docker
Kubernetes
GitHub Actions
GitLab CI/CD
Infrastructure as Code
Terraform
Cloud infrastructure
AWS
Azure
GCP
Serverless applications
Multi-cloud deployment
Container security
Platform engineering

The course shows how AI can assist with automation, configuration generation, troubleshooting and infrastructure documentation.

AI for SRE, Observability and Incident Management

Explore how AI can support Site Reliability Engineering (SRE) and production operations.

Topics include:

Service Level Objectives
Error budgets
Observability
Prometheus
Grafana
OpenTelemetry
Logging
Distributed tracing
Incident runbooks
Failure analysis
Disaster recovery
High availability
Incident-response automation
Performance engineering

These skills connect Generative AI with real production-engineering environments.

Generative AI for Secure Coding and Application Security

Security is integrated throughout the software-development lifecycle.

You will explore:

Secure code generation
Threat modeling
Vulnerability analysis
SAST
DAST
OWASP-oriented testing
Penetration-testing automation
Secure APIs
Zero Trust
Container security
Data privacy
Security compliance
Enterprise AI security

Generative AI can accelerate security analysis, but security outputs must be validated using established tools, controls and human expertise.

1000+ Software Engineering AI Prompts

The course includes a dedicated library of 1000+ practical software-engineering prompts covering areas such as:

Backend development
Frontend development
REST APIs
Database design
Microservices
Authentication and authorization
DevOps
Docker
Infrastructure as Code
Cloud architecture
Application security
SAST and DAST
API security
Observability
Testing
Performance
Mobile development
Design patterns
Legacy modernization
Event-driven architecture
Git
Machine-learning pipelines
Kubernetes
SRE
Domain-Driven Design
Platform engineering

These prompts can be adapted across ChatGPT, Claude, Gemini, Microsoft Copilot and other compatible Generative AI tools.

1000+ AI-Native Software Engineering Prompts

The course also contains a second advanced library of 1000+ AI-native software-engineering prompts.

This section moves beyond ordinary AI-assisted coding into the engineering of modern AI-powered systems.

Topics include:

Retrieval-Augmented Generation (RAG)
Vector databases
Semantic search
Embeddings
LangChain
LangGraph
AI Agents
Agentic AI
Multi-agent systems
Enterprise knowledge assistants
Codebase understanding
AI-assisted reverse engineering
Requirements analysis
Software traceability
Prompt evaluation
Prompt versioning
Prompt security
Prompt injection defense
LLM observability
Hallucination detection
LLM evaluation
AI cost optimization
AI gateways
Model routing
Local LLM deployment
Fine-tuning
LoRA and QLoRA
Model Context Protocol (MCP)
AI tool calling
Enterprise search
Knowledge graphs
AI-native application architecture
AI product engineering
AI copilot development
AIOps
SecOps
AI red teaming
AI governance
Responsible AI
AI risk management
Enterprise AI security
Autonomous DevOps agents
Autonomous incident-response agents
Enterprise agentic systems

This section is designed to help software professionals understand the rapidly evolving architecture of AI-native and agentic software systems.

20 Practical AI Agent & Multi-Agent Projects

The course includes 20 practical AI Agent projects for software developers.

Instead of treating every AI agent as a chatbot, these projects demonstrate how agents can retrieve information, use tools, maintain workflow state, generate outputs, coordinate with other agents, evaluate responses and support real software-development tasks.

Projects include:

Automated Code Generation, Execution & Debugging Agent
Retrieval-Augmented Code Generation & Q&A Agent
Qdrant-Based Code Generation & Q&A Agent
LangGraph Code Assistant
Coding & Planning Multi-Agent System
README Generator Agent
Markdown Validator Agent
Landing Page Generator Agent
Natural Language-to-SQL Agent
LangGraph SQL Agent
Data Visualization Multi-Agent System
Web Search Tool Agent
Three-Agent Group Chat System
Supervisor Multi-Agent Workflow
Information Gathering Agent
Customer Support Graph Agent
RAG Group Chat Agent
AgentEval Multi-Agent Assessment System
AgentOps Observability Agent
AgentBuilder Auto-Generated Multi-Agent System

These projects introduce important patterns used in modern AI Agent, Agentic AI and multi-agent application development.

What You Will Be Able to Apply

By working through the course, you will be able to apply Generative AI across:

Software development
Code generation
Debugging
Code review
Software architecture
Testing
DevOps
CI/CD
Cloud engineering
SRE
Observability
Application security
RAG applications
AI Agents
LangGraph workflows
Multi-agent systems
MCP
AI tool calling
LLM evaluation
AI security
AI governance
AI-native application development

Who Should Take This Course?

This course is designed for:

Software Engineers
Software Developers
Application Developers
Backend Developers
Frontend Developers
Full-Stack Developers
Software Architects
DevOps Engineers
Cloud Engineers
SRE Professionals
Platform Engineers
Application Security Professionals
AI Engineers
Generative AI Developers
Technical Leads
Engineering Managers
Developers building AI-powered applications
Professionals interested in AI Agents and Agentic AI

Whether you want to improve existing software-engineering workflows or develop modern RAG, AI Agent and AI-native applications, this course provides a practical foundation for applying Generative AI across the modern software-development lifecycle.

Who this course is for:

Software Engineers aiming to leverage Generative AI tools for faster and smarter coding, architecture, and DevOps workflows.
Backend Developers who want to auto-generate APIs, improve system designs, automate deployments, and optimize application performance using AI.
Frontend Developers interested in using AI to boost UI/UX workflows, auto-generate React/Vue components, and optimize frontend performance.
DevOps Engineers looking to automate infrastructure creation, monitoring setups, and CI/CD pipelines with Generative AI prompts.
Solution Architects wanting to use AI for generating architecture diagrams, conducting trade-off analysis, and suggesting technology stacks.
Quality Assurance (QA) Engineers and Test Automation Engineers who wish to auto-generate unit tests, API tests, E2E tests, and fuzz tests using GenAI.
Security Engineers aiming to integrate secure code generation, vulnerability scanning, and compliance automation into the software lifecycle.
Technical Leads and Engineering Managers seeking to empower their teams with AI-driven development accelerators and best practices.
Students, Learners, and Fresh Graduates in Computer Science or related fields aspiring to learn the future of AI-driven software engineering.
Anyone curious about applying Generative AI in practical, real-world coding, testing, architecture, and DevOps workflows.

Generative AI for Software Engineers & Developers

What you'll learn

Explore related topics

Course content

Introduction5 lectures • 12min

Large Language Models (LLMs), Fine-Tuning & APIs for Developers5 lectures • 12min

Prompt Engineering for Coding & Software Development5 lectures • 12min

Generative AI for Software Architecture, System Design & Microservices5 lectures • 13min

AI Code Generation, Refactoring, Clean Code & Documentation5 lectures • 12min

Generative AI for Code Review, Static Analysis & Bug Detection4 lectures • 10min

Generative AI for Software Testing, Test Automation & QA5 lectures • 12min

Generative AI for DevOps, CI/CD, Docker, Kubernetes & Infrastructure4 lectures • 10min

AI for SRE, Observability, Reliability & Incident Management4 lectures • 10min

Generative AI for Secure Coding, AppSec, Threat Modeling & Compliance4 lectures • 10min

Requirements

Description

Who this course is for: