Grokking the Generative AI System Design

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Common models include variational autoencoders (VAEs), generative adversarial networks (GANs), large language models like GPT, and diffusion models like Stable Diffusion and SORA. These models use different techniques to generate data. We choose a model based on the use case; for example, GANs are typically used to generate images.

Examples include text-to-text generation systems like ChatGPT and Gemini, text-to-image generation tools like DALL•E and Midjourney, text-to-speech systems like ElevenLabs, and text-to-video generation systems like Mochi 1 and SORA. These systems showcase the diverse applications of generative AI.

You can prepare for a GenAI System Design interview by studying the core AI and ML concepts, practicing System Design problems, reviewing common interview questions, and building a strong portfolio of projects. Mock interviews can also be beneficial. You should also learn design frameworks like the SCALED approach to solve unseen problems during the interview.

We use evaluation metrics (automated and human) to test the performance of GenAI systems. They vary depending on the application. Common methods include measuring accuracy, diversity, fluency, and coherence. Common metrics include BLEU score, CLIP score, ROGUE score, Fréchet inception distance (FID), and mean opinion score (MOS).

Generative AI and machine learning are predictive methods but focus on different things. Machine learning makes discriminative predictions, like classifying data, while generative AI makes generative predictions, creating new content. Both learn from data and improve over time, but machine learning focuses on recognizing patterns, while generative AI uses those patterns to generate new data. They represent two powerful branches of AI, each with unique applications and capabilities.

To excel in a generative AI interview, clearly explain your reasoning using examples of case studies. Choose appropriate data and models, like GPT for text, and detail the training process, including techniques like fine-tuning. Finally, outline a robust deployment System Design, showcasing how the model integrates into a real-world system, like a conversational chatbot AI.

Online courses like “Grokking the Generative AI System Design” provide a solid foundation in the core concepts. Supplement this with research papers and blogs to stay updated on the latest advancements. Then, you should analyze real-world systems like ChatGPT or DALL•E to understand their design choices. Finally, practice designing systems for common generative AI tasks (text-to-text, text-to-image, etc.), exploring different solutions and their tradeoffs to deepen your understanding.