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- AI
Generative AI: Engineering and Architecture
This learning path is actively in production. More content will be added to this page as it gets published and becomes available in the library. Planned content includes: - GenAI System Architecture Overview - GenAI Data and Knowledge Layer - GenAI Retrieval and Memory Patterns - GenAI Model Access Layer and Structured Outputs - GenAI Orchestration and Agent Patterns - GenAI Inference and Serving Architecture - LLMOps: Evaluation, Observability and Quality - Safety and Security Architecture for GenAI Systems - Reliability, SLOs and Incident Management for GenAI Systems - Platform and Governance for GenAI Systems - Generative AI: Engineering and Architecture (Skill IQ)
Generative AI systems power intelligent applications through sophisticated architectures combining large language models, retrieval systems, and orchestration layers. This comprehensive learning path teaches engineers, architects, and technical leaders how to design, build, and operate production-ready GenAI systems at scale—covering everything from system architecture and data foundations to deployment, reliability, and governance.
This path covers modern GenAI engineering practices including RAG architectures, multi-agent systems, vector databases, function calling, fine-tuning strategies, cost optimization, and enterprise governance. You'll learn to build systems using current models and frameworks (OpenAI GPT-4, Anthropic Claude, open-source alternatives) and production tools (LangChain, vector databases, observability platforms). By completing this path, you'll gain the skills to architect and operate reliable, cost-effective GenAI systems that scale from prototype to production.
Content in this path
Generative AI
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What You'll Learn
- How to design and build production GenAI systems including architecture patterns, data foundations with embeddings and vector databases, advanced RAG pipelines, and multimodal integration
- How to orchestrate intelligent agents with multi-step reasoning and tool integration, while optimizing inference performance through batching, GPU scaling, and cost-effective serving strategies
- How to operate systems reliably with comprehensive LLMOps including automated testing, continuous evaluation, observability, and incident management
- How to ensure production readiness through security architectures, content moderation, bias mitigation, governance frameworks, and regulatory compliance
- Learners are expected to have basic knowledge of generative AI and LLMs, software engineering experience (Python preferred), and familiarity with APIs, databases, and cloud infrastructure. Understanding of system architecture and deployment concepts is required. This intermediate-to-advanced path is designed for engineers and architects building production GenAI systems.
- Generative AI
- Large Language Models
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