Generative AI System Design

“Generative AI is the most powerful tool for creativity that has ever been created. It has the potential to unleash a new era of human innovation.” – Elon Musk.

Let’s explore the power of generative AI. Imagine you’re planning your next summer vacation trip with your family, and you describe an imagined location loudly: a cozy beach house, golden sands, and a breeze that smells like freedom. You describe it, and within moments, an AI transforms your words into a vivid image, crafts a custom itinerary, and even suggests matching places.

This is not science fiction. It’s a glimpse into a future already unfolding—all thanks to generative artificial intelligence (GenAI). Generative AI is reshaping what’s possible, from tools that create stunning visuals to those that write entire books, compose music, or simulate virtual worlds.

But here’s the kicker: all the magic hinges on System Design—the behind-the-scenes blueprint that makes these creative marvels functional. So, how do we design a generative AI system that transforms dreams into reality while addressing real-world challenges? For that, we need to understand GenAI from the System Design perspective.

Let’s start with understanding what generative AI means.

What is generative AI?

Generative AI, or GenAI, is a field of artificial intelligence that creates new content, such as text, images, videos, or music, based on patterns learned from data. Unlike traditional AI, which focuses on analysis and predictions, GenAI generates something new using advanced AI models, offering creativity, problem-solving, and automation tools. GenAI doesn’t just replicate; it innovates and is adaptable. Whether you’re an artist, a business owner, a developer, or a student, GenAI tailors its capabilities to meet your needs.

The generative AI market is expected to grow at a CAGR (compound annual growth rate) of 42% over the next 10 years to become a $1.3 trillion market by 2032.

What is generative AI system?

A generative AI (GenAI) system is the practical application of GenAI models integrated with data, infrastructure, and user interfaces to produce real-world outputs. For example, text-to-image systems generate visual art, while text-to-speech systems produce lifelike audio, enabling real-world applications across industries like design, healthcare, education, and entertainment.

To better understand how generative AI is already transforming industries, here are some examples showcasing real-world GenAI applications:

When discussing designing GenAI systems, we must understand how to align GenAI models with System Design. Let’s explore.

What is generative AI System Design?

Generative AI System Design is the combination of GenAI and traditional System Design. It involves creating the frameworks and infrastructure that allow AI models to generate meaningful, efficient, and ethical outputs. It integrates AI models, data pipelines, user interfaces, and computational resources into a cohesive system that meets specific goals. While traditional System Design principles ensure performance and reliability, advancements in machine learning infrastructure, like distributed machine learning for training and inference, help meet the resource-intensive demands of GenAI models.

GenAI System Design isn’t about making GenAI possible; it’s about optimizing it for impact, ensuring these systems meet the growing demand of industries like healthcare, education, entertainment, etc. while staying ethical, scalable, and user-friendly. Generative AI System Design is the backbone of tomorrow’s AI-driven solutions.

Now, let’s explore how to design such GenAI Systems.

How to design a GenAI System?

Designing a GenAI system isn’t just a technical challenge; it’s a creative and strategic process. Thinking about the big picture and laying a solid foundation before diving into technical details or System Design is crucial. Let’s list the important steps to consider:

• The purpose: What is our system meant to achieve? A well-defined goal establishes the basis for every design decision, whether generating realistic images, assisting with writing, or creating personalized experiences.
• The users: Who will use this system and how? A tool for developers may prioritize flexibility, while one for end-users needs simplicity and accessibility.
• The output: What type of content will our system generate, and what standards must it meet? Each type, such as text, image, audio, etc., requires specific considerations for quality and relevance.

Once we understand the foundational requirements, we can develop a practical framework for designing a GenAI system. A step-by-step framework that starts by defining the system’s purpose and progressively addresses the challenges of building a scalable, efficient, and robust solution. To streamline this process, we’ve developed the SCALED framework, outlined below, to guide you through each critical stage of System Design:

Challenges in designing the GenAI system

Despite the framework for seamlessly designing a scalable system, we need to understand the challenges that can be faced despite having a scalable and well-designed system, as follows:

• Data availability and quality: Generative AI depends on high-quality, diverse data. However, locating or curating datasets that reflect your use case can be time-consuming and challenging.
• Balancing performance and cost: GenAI models, especially large ones, can be expensive to train and deploy. We’ll need to optimize resources without compromising on quality.
• User expectations: Users expect generative systems to be fast, accurate, and creative–all at once. Designing a system that meets these expectations while staying practical is challenging.
• Ethical considerations: We must address output bias and prevent misuse of GenAI systems. Navigating these ethical challenges is a complex task but an essential part of System Design.
• Integrating with existing systems: Many GenAI systems must work alongside other tools or platforms, requiring careful planning for seamless integration.

Considering these challenges, let’s understand the process of designing a GenAI system.

GenAI System Design: A conversational system

We’ll design a GenAI system based on a conversational large language model (LLM) that inputs text and generates a textual response. But before we do so, let’s first understand the system’s requirements.

Functional requirements

• Natural language understanding: The system should be able to interpret user input and identify user intent, extract important entities, and understand context through sentiments in the input.
• Natural language generation: After understanding and interpreting the input and context, the system should respond accurately to the user’s query.
• Personalization and context management: The system should be able to tailor responses based on user preferences and past interactions and manage the context of current conversations.
• Content moderation: The system should prevent harmful or inappropriate content generation by using filters and flags to detect sensitive or prohibited language to maintain a safe and respectful environment.

Nonfunctional requirements

We can consider the following nonfunctional requirements:

• Low latency: The system should provide responses or output in the shortest possible time.
• Scalability: The GenAI systems are expected to handle millions of users and billions of requests, so they should scale accordingly.
• Availability: The system should be available and operational for users to use anytime by minimizing downtime as much as possible.
• Reliability/data integrity: The system’s output or response should be bias-free and accurate.

Let’s now talk about the System Design to achieve these requirements.

Architecture and workflow

On a high-level design of a conversational system, the user’s query via text is sent to a prompt embedding system where tokenization and normalization occur to clean and prepare the text for processing. The system identifies the user’s intent using natural language understanding (NLU). For example, a query like “What’s the weather in London?” triggers a weather-check intent. The embedding system checks the cache to see if it received a recent similar query, eliminating the steps to process it and reducing response time.

If no cached data is found, the system processes the query to determine the session-specific and historical context to personalize responses. The system may need external data to generate an accurate response, and for that, the system integrates with databases, APIs, or knowledge graphs. For example, querying a weather API for a location-specific forecast.

The processed input and the context are passed to the generation system model (e.g., GPT, LLaMA, etc.), trained for our specific use case. The model generates a response based on patterns it has learned. The system adapts responses based on user-specific data such as preferences, language style, etc. To ensure clarity, accuracy, and quality, the data undergoes post-processing through a response validation system, which performs the following tasks:

• Filters out irrelevant or inappropriate content.
• Formats the output to match the requirements of the delivery medium.

The GenAI system then sends the response back to the user. The user can optionally provide feedback to assess the response quality and help improve the system over time.

The following is a high-level System Design of a text-to-text generation system:

Note: Generative AI systems are typically designed to run on centralized application servers. However, some use cases require high performance, in which generative AI systems may adopt decentralized or edge computing architectures.

Let’s examine the details of the few important components enabling the GenAI system to work seamlessly.

Intent and context management

For intent and context management, the system uses the following:

• The system uses knowledge graphs for context management by creating structured relationships between entities mentioned in user input. For example, if a user asks, “Give me some famous landmarks near Central Park,” the system can quickly retrieve the graph to identify Central Park as a landmark in New York and retrieve nearby entities, making responses more intelligent and context-aware.
• The system uses embeddings to capture session-specific context and long-term conversational history, making interactions more personalized and human-like.

Prompt processing and knowledge retrieval

Prompt processing systems combine traditional search, such as keyword-based search, with semantic retrieval, such as vector similarity search. For example, a user asks, “What’s the tallest building in New York, and how far is it from Central Park?” The system would process it as follows:

• The system identifies “tallest building” and “New York City” to understand the intent and retrieves “One World Trade Center” using semantic embeddings.
• Factual data like the building’s height (1,776 feet) is fetched from structured databases or APIs with a traditional search method, which also locates Central Park and calculates its distance using APIs like Google Maps.
• The results are combined into a complete response with context and precision.

The final output is sent to the user: “The tallest building in New York City is One World Trade Center, standing at 1,776 feet. It’s about 8.2 miles from Central Park.”

Response generation and post-processing

The system uses inference servers as the backbone for processing user queries through the generative model. Optimization techniques like model quantization and model sharding ensure real-time responsiveness, even for computationally expensive tasks like generating long-form answers.

It also employs real-time moderation filters powered by classifiers or rules to prevent biased, harmful, or sensitive content from being generated.

Integrating all these techniques enables the GenAI system to handle peak load seamlessly and generate accurate responses according to the context.

Using the design above, we have only scratched the surface of a text-to-text generation system. In reality, there are several other design aspects to look at, such as:

• How the system extracts entities and manages dialogues.
• How the data preprocessing, model training, and testing are performed.
• How to estimate resources required (e.g., inference servers, model size, etc.) for training and deployment of the service.
• How prompts are enhanced, and long-term memory is maintained, etc.

You can explore the nitty-gritty details behind conversational and other GenAI systems discussed below by exploring the System Design of real-world generative applications in Grokking the Generative AI (GenAI) System Design course.

Designing other GenAI systems

We have considered the following unique aspects of other GenAI systems in the course:

• **Text-to-Image: **This system requires a multi-modal understanding to generate visuals from text, focusing on feedback-driven refinement and managing high computational demands for quality output.
• Text-to-speech: This system combines linguistic models with acoustic precision, emphasizing prosody and intonation and handling complex linguistic inputs for natural, expressive speech.
• Text-to-Video: The generation system integrates text, images, and audio to produce videos, tackling challenges like temporal consistency and synchronization while optimizing costs and performance.

Generative AI systems are complex and sophisticated. They must balance performance, ethical concerns, and real-world constraints; each application’s unique aspects add to the challenge.

Future directions in GenAI System Design

Several exciting trends shape the future of the GenAI System Design that we should consider while designing our systems:

• Next-generation models are evolving to be more efficient, with multimodal systems capable of simultaneously processing and generating text, images, and even videos. These models will also become more compact, requiring fewer resources to deliver powerful results. To stand out in the market, we should consider our system capable of offering all these features.
• The democratization of AI is on the rise, with tools and platforms enabling non-experts to build and use generative systems while open-source innovations continue to drive rapid advancements. We should consider the design accordingly.
• Personalized GenAI is expanding, creating systems that adapt to individual needs and offer tailored experiences in sectors like healthcare, education, and creativity.

We must balance creativity, accuracy, and ethics and should pursue future trends while designing GenAI systems.

Conclusion

The real power of GenAI lies in the sophisticated System Design that supports it, balancing creativity with efficiency and scalability while addressing ethical concerns. As we look to the future, the challenge is designing a powerful and responsible system that meets diverse industry demands ethically and is user-friendly. By mastering GenAI System Design, we can harness this technology for impactful solutions spanning healthcare, education, and more.

Happy learning!