Generative AI for Software Engineers & Developers
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Unclaimed ProfileThe 'Generative AI for Software Engineers & Developers' course by Edcorner Learning is a comprehensive, practical guide designed to help modern developers integrate artificial intelligence directly into their daily workflows. Spanning across crucial development phases, this program provides learners with an extensive library of over 1000 expert-crafted prompts specifically tailored to accelerate coding, system architecture, DevOps, testing, and security tasks. Students will move beyond basic chat interfaces to master advanced prompt engineering techniques, including Zero-shot, Few-shot, Chain of Thought, and Tree of Thought methodologies. Designed for software engineers, architects, and DevOps professionals of all skill levels, the curriculum covers real-world application scenarios. You will learn how to design robust system architectures, generate clean code that adheres to SOLID and DRY principles, automate multi-framework testing pipelines, and deploy secure Infrastructure as Code utilizing Docker, Kubernetes, and Terraform. Additionally, the course emphasizes security practices such as vulnerability detection and compliance documentation. By bridging the gap between theoretical AI concepts and hands-on software engineering practices, this course empowers developers to dramatically boost their productivity and build high-quality software systems faster.
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Edcorner Learning
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Edcorner Learning is a premier technology training and upskilling hub dedicated to bridging the gap between cutting-edge Artificial Intelligence developments and practical, industry-specific applications. With a specialized focus on Generative AI and Predictive AI Architecture, Edcorner Learning designs comprehensive curricula tailored to various modern professional roles. Whether you are a software developer looking to integrate LLMs into your codebase, a finance analyst striving to automate complex predictive models, a product manager aligning AI capabilities with market demands, or an automotive engineer navigating software-defined vehicles, Edcorner Learning provides targeted, hands-on instruction. Their teaching methodology centers on practical, scenario-based learning, stripping away…Show more
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Program Overview
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Learning format
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Price
Price may change · updated within 1–2 weeks
Course language
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).
Best For
- Software engineers and developers looking to integrate AI into their daily coding workflow.
- DevOps and SRE professionals aiming to automate infrastructure, monitoring, and CI/CD pipelines.
- System architects wanting to leverage AI for rapid design modeling and trade-off analysis.
- Security-conscious developers interested in using AI for vulnerability detection and compliance documentation.
Not For
- Absolute beginners with no prior programming or software development experience.
- Data scientists or AI researchers looking for deep mathematical theory or model training techniques.
- Professionals seeking a guide on how to build their own Large Language Models from scratch.
- Managers or non-technical stakeholders who do not need to apply AI to code or infrastructure tasks.
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