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Reseña del curso Generative AI for Software Engineers & Developers de Edcorner Learning

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1000+ GenAI Prompts to Accelerate Your Software Engineering Journey

Formato de aprendizaje Online Course

Subcategoría Generative AI

Precio del curso $19.99 (list $9.99)

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Sobre Edcorner Learning

1000+ GenAI Prompts to Accelerate Your Software Engineering Journey

¿Quién es Edcorner Learning?

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Total de estudiantes

5,900 students

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Idioma del curso

English

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¿Qué se aprende en el curso de Edcorner Learning?

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).

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