ai architecture
Architecting Generative AI Solutions
- Last updated 30/03/2025
Overview
Learn how to design and implement robust Generative AI solutions that align with business goals, address real-world challenges, and scale securely across enterprise environments. This course covers key architectural patterns, technology choices, integration strategies, and best practices for delivering Gen AI-driven impact.
Whether you’re building conversational agents, automating content generation, or enhancing knowledge discovery with large language models, you’ll gain the insights needed to navigate the unique complexities of Generative AI. Explore how to balance creativity with control, leverage pre-trained models effectively, and move from experimentation to production while ensuring ethical use, performance, and cost efficiency.
Key Points
- Gain a deep understanding of the core components and layers that make up a scalable Generative AI solution architecture.
- Explore advanced techniques in data preparation, prompt engineering, and fine-tuning to optimize model performance.
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- Design solutions with built-in scalability, enterprise-grade security, and responsible AI principles from the ground up.
- Implement retrieval-augmented generation (RAG), agent frameworks, and orchestration patterns for complex workflows.
- Master the transition from proof of concept to production with robust monitoring, governance, and lifecycle management practices.
Course Lessons
An overview of Generative AI fundamentals and how industry-specific architectures are shaping real-world applications.
A practical guide to orchestration frameworks that streamline and scale Generative AI workflows and applications.
An introduction to vector stores and indexing techniques for efficient retrieval in Generative AI solutions.
A hands-on look at prompt engineering techniques to optimize the performance and outputs of Generative AI models.
A guide to building an AI evaluation framework for measuring quality, safety, and performance of Generative AI systems.
An exploration of key security and privacy considerations when designing and deploying Generative AI solutions.
A deep dive into LLMOps practices for deploying, monitoring, and managing large language models in production environments.
A walkthrough of how to build AI-powered applications by integrating front-end interfaces with back-end Generative AI services.
A summary of proven best practices for designing robust, scalable, and ethical Generative AI solutions.
A hands-on capstone project where you apply end-to-end Generative AI solution design to a real-world use case.
Instructor
Lead Generative AI Solutions Architect
This course includes:
- 12 hours on-demand video
- Full lifetime access
- Access on mobile, tablet, desktop
- Access to dedicated Discord channel for ongoing support and knowledge sharing
- Certificate of completion