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Horses for Courses: Decoding the Generative AI Technology Stack

Howard Lee

Jan 22, 2024

In the domain of Generative AI, a structured multi-layered architecture forms the basis for a variety of applications. The technology stack, essential to this domain, is arranged in layers, each with a specific function, ranging from providing the computational power required to operate large-scale AI models to the detailed processes of model refinement. This attached architecture [credit: Menlo Ventures] facilitates a range of uses, enabling both individual developers and large organizations to employ artificial intelligence effectively.

The field of Generative AI operates on a complex stack of technologies, where each layer plays a critical role in converting conceptual ideas into operational applications. This stack reflects advances in the field, illustrating a development arc that spans from the creation of broad-capability models to the deployment of intricate tools for monitoring and managing AI systems. The exploration of each layer sheds light on the varying degrees to which different industries integrate these technologies, aligning with their specific operational requirements and strategic goals. The practical application of the Generative AI stack demonstrates the adaptability and breadth of AI technologies.

Layer 1: Compute + Foundation is the fundamental level where large-scale AI models, known as Foundation Models, are developed and housed.  It's where Foundation Models like OpenAI's GPT and Google's BERT reside. These models are expansive programs that have been trained to understand and generate content based on patterns learned from massive datasets. They are versatile and can be customized for various tasks through additional training. The deployment and inference processes within this layer involve setting up these models on servers or cloud platforms like AWS or Google Cloud so they can process incoming data and generate responses in real-time. GPU providers are crucial here as they supply the computational power needed to handle these intensive tasks.

Layer 2: Data  is where the groundwork for AI's understanding and generation of content is laid. It involves the crucial task of data pre-processing, which is akin to organizing and cleaning your workspace before starting a complex project. This step ensures that the data is in the right format and quality for the AI models to learn from effectively.

Here, we also encounter the concept of embeddings, which are a method for converting different types of data, such as words or images, into numerical values that AI can understand and process. These embeddings are stored in vector databases, specialized storage systems that allow for the efficient retrieval of these numerical representations. Think of vector databases as an advanced filing system that organizes and retrieves documents by their content, enabling the AI to quickly find the information it needs to perform tasks such as answering questions or recommending products.

This data layer is essential because the better the organization and quality of data at this stage, the more effectively the AI can learn and the more accurate its outputs will be.

Layer 3: Deployment encompasses tools and systems that allow for the practical use of AI models. Prompt management tools help users formulate queries or inputs that guide AI models to produce desired outputs. Agent tool frameworks provide the necessary infrastructure for integrating AI functions into software applications, and orchestration platforms ensure that all parts of the AI system work together smoothly.

  • Prompt Engineering:

    • Template libraries: Access to pre-built prompts for common tasks like writing different kinds of creative content, summarizing information, or generating code.

    • Parameter tuning: Adjusting elements like temperature, top-k sampling, and repetition penalty to control the diversity and coherence of generated text.

    • Prompt optimization tools: Automated algorithms that iteratively refine prompts to achieve desired outputs.

  • Orchestration:

    • Workflow automation: Streamlining multi-step processes involving data pre-processing, prompt generation, model execution, and post-processing.

    • Collaboration tools: Enabling multiple users to collaboratively refine prompts and manage generative AI workflows.

    • Version control and deployment: Tracking changes made to prompts and models, and seamlessly deploying new versions into production environments.

Layer 4: Observability deals with monitoring and securing AI systems. Tools in this layer are designed to track the performance of AI applications, assess how well they are functioning, and ensure they are secure from potential cyber threats.

  • Observability:

    • Model performance monitoring: Tracking metrics like accuracy, latency, and resource utilization to identify potential issues.

    • Explainability tools: Understanding the reasoning behind model outputs and debugging unexpected results.

    • Data lineage tracking: Tracing the origin and transformations of data used to train and run the model, ensuring compliance and responsible AI practices.

  • Evaluation:

    • Human-in-the-loop evaluation: Manually assessing the quality and suitability of generated content for specific tasks.

    • Automated metrics: Using pre-defined criteria like factual accuracy, grammatical correctness, or stylistic consistency to evaluate outputs.

    • A/B testing: Comparing different models or prompts to determine their effectiveness in achieving desired goals

  • Security:

    • Bias detection and mitigation: Identifying and addressing potential biases in model outputs to prevent discriminatory or harmful content.

    • Adversarial attack defense: Protecting models from malicious attempts to manipulate their outputs through crafted inputs.

    • Data privacy and security: Ensuring data used in the generative AI process is protected from unauthorized access or misuse.

Each layer is vital for building robust AI applications, offering both specialists and generalists the tools they need to innovate and implement AI solutions effectively.

The generative AI stack is modular, and different applications will need different components depending on various factors like user, scale, and complexity. Here are a few examples of how different stack configurations might be used in different use cases:

Student writing an essay:

  • Stack configuration: Closed-source LLM only (e.g., OpenAI API)

  • Reasoning: The student needs a simple and easy-to-use tool to help them with writing. A closed-source LLM can provide good results without the need for complex data pre-processing, prompt engineering, or fine-tuning.

Company developing a knowledgebase chatbot:

  • Stack configuration: RAG approach with a vector database

  • Reasoning: The company needs a chatbot that can access and process a large amount of information. The RAG approach allows the chatbot to be trained on a massive dataset of text and code, and the vector database provides a efficient way to store and retrieve information.

Researcher developing a new generative AI model:

  • Stack configuration: Full generative AI stack, including data pre-processing, prompt engineering, fine-tuning, and evaluation

  • Reasoning: The researcher needs to have complete control over the generative AI process in order to develop a new model. The full stack provides them with the tools and flexibility they need to experiment and iterate.

Artist creating generative art:

  • Stack configuration: Closed-source LLM with custom prompts and fine-tuning

  • Reasoning: The artist wants to create unique and creative artwork. Using custom prompts and fine-tuning, they can control the style and output of the generative AI model.

Small Business Owner Creating Marketing Copy:

  • Stack configuration: Simple API + pre-written prompts

  • Reasoning: The owner needs quick and easy marketing materials but lacks technical expertise. An API with pre-written prompts for product descriptions, social media posts, or ads lets them generate content without building complex pipelines.

Healthcare Startup Training a Conversational AI Assistant:

  • Stack configuration: Open-source LLM + customized data pre-processing + fine-tuning

  • Reasoning: The startup needs a personalized AI assistant for patient interactions. Open-source LLMs offer cost-effective base models, while data pre-processing tailored to medical records and fine-tuning with healthcare data ensure accurate and sensitive responses.

Large Media Company Generating Personalized News Summaries:

  • Stack configuration: Full generative AI stack with RAG approach and user profiling

  • Reasoning: The company wants to engage readers with tailored news digests. The RAG approach leverages their vast news archive, and user profiling personalizes summaries based on reading habits and interests.

International Non-Profit Translating Educational Materials:

  • Stack configuration: Closed-source LLM with domain-specific fine-tuning

  • Reasoning: The non-profit needs to translate resources for education access in under-resourced regions. Domain-specific fine-tuning with education datasets improves translation accuracy for technical terms and cultural nuances.

Manufacturing Company Optimizing Production Processes:

  • Stack configuration: Generative AI + simulation environment + real-time data feedback

  • Reasoning: The company wants to optimize production efficiency and resource utilization. Generative AI models predict potential outcomes based on various scenarios, while a simulated environment tests these predictions, and real-time data feedback refines the models for continuous improvement.

The Generative AI technology stack is a catalyst for innovation across industries, disciplines, and individual pursuits. The layered architecture we've discussed provides a roadmap for entities of all sizes and technical proficiencies to leverage AI for a multitude of applications, from automating mundane tasks to solving complex, industry-specific challenges.

As we've seen through various use cases, the stack’s modular design allows for a high degree of customization and flexibility. Whether it's a student requiring assistance with essay writing or a large corporation personalizing customer experiences at scale, the stack offers the necessary tools and frameworks to meet these needs effectively and efficiently. As the technology matures and becomes more accessible, we can expect to see even more innovative uses of AI, each finding its foundation within this stack.

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