Challenges of evaluating the generative AI tasks - Introduction
1. Challenges of evaluating the generative AI tasks - Introduction
Now that you're aware of model evaluation's critical role in traditional machine learning, let's unlock the exciting world of generative AI, where models can create realistic images, compose new music, or generate compelling stories. With this innovative power, comes the challenge of evaluation, which is especially crucial for ensuring the reliability and quality of generative AI outputs. Though both are forms of AI, there is a distinction between non-generative and generative AI. While non-generative or predictive AI focuses on analyzing existing data to make predictions, classifications, or decisions, generative AI creates entirely new content by learning patterns from vast datasets. Therefore, the output is more open-ended and creative. While predictive and generative AI share some MLOps principles, evaluating generative AI presents novel challenges due to its creative nature. Let's narrow the focus to large language models, or LLMs, for short, which are a specific type of generative AI model that's revolutionizing the way we interact with language. While LLMs and generative AI are often used interchangeably and both refer to AI models that generate human-like text, they're not quite the same thing. Generative AI is a broader term that encompasses models capable of generating various types of content beyond text, such as images, music, or even code. LLMs are a subset of generative AI, specializing in language tasks like generating text, translating languages, and summarizing information. For our purposes in this course, we'll focus primarily on LLMs to keep our discussions simple and focused. Before evaluating LLMs in production, it's helpful to understand the fundamental components that are involved throughout the entire life cycle, which we can call the LLM block. Throughout the life cycle, evaluation plays a vital role. In generative AI, the journey begins with selecting the right pretrained model. This is a foundational decision that demands data-driven insights. At the heart of the language model ecosystems are the LLMs. These models interact with various components like data sources, prompt templates, memory, tools, agent control flows, and guard rails. Each component plays a crucial role in shaping the model's behavior and output, making the overall system more complex than traditional machine learning models. LLMs are the core reasoning engine, accessible via APIs from platforms like Google, or open source alternatives like Mistral. Data truly serves as the cornerstone of the entire process. Data sources provide contextual information through various sources like relational, graph, and vector databases, which are crucial for retrieval, augmented generation. Prompt templates consist of standardized instructions given to the model. They're shared across requests and typically versioned and managed akin to code using formats like the prompt file. Memory functions as a dynamic data source, storing and retrieving past interactions with the model for context and subsequent requests. Tools extend the model's capability by enabling interactions with external systems, such as API calls, code execution, and other integrations. Agent control flow enables the model to iteratively refine its approach to a task, making multiple attempts until predefined stopping criteria are met. Guard rails, as the name suggests, are safety mechanisms applied to the model's output before it reaches the user. Ranging from simple logic, like keyword detection, to invoking secondary models. These measures can trigger fallback to human review when necessary. These individual components present a vast and distinctive design space to explore, requiring careful configuration and consideration. It's a paradigm shift from traditional model evaluation, which primarily focuses on optimizing model parameters and hyperparameters to enhance generalization and predictive performance on unseen data, rather than orchestrating the intricate interaction of diverse components.2. Let's practice!
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