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The Shift from Models to Compound AI Systems

๐ŸŒˆ Abstract

The article discusses the shift from focusing on large language models (LLMs) as the primary ingredient in AI application development to the increasing use of compound AI systems, which combine multiple components to achieve state-of-the-art results. It analyzes the reasons behind this trend, the challenges in developing and optimizing such compound systems, and emerging paradigms to address these challenges.

๐Ÿ™‹ Q&A

[01] Why Use Compound AI Systems?

1. What are the key reasons developers are building compound AI systems instead of relying solely on large language models?

  • Some tasks are easier to improve via system design rather than just scaling up the language model
  • Compound systems can be more dynamic and incorporate timely data, unlike static language models
  • Compound systems provide better control and trust compared to standalone language models
  • Compound systems allow for more flexibility in performance goals and cost constraints

2. How does the shift to compound AI systems in generative AI match trends in other AI fields like self-driving cars? The article notes that most state-of-the-art implementations in other AI fields, such as self-driving cars, are also compound systems with multiple specialized components, indicating that the shift to compound systems is a broader industry trend.

[02] Developing Compound AI Systems

1. What are some key design challenges in building compound AI systems?

  • The vast design space of possible system configurations, with choices around model selection, retrieval techniques, and ways to combine components
  • Allocating limited resources like latency and cost budgets across the different system components
  • Co-optimizing the components to work well together, as the system is not fully differentiable end-to-end

2. What are some of the operational challenges in running compound AI systems?

  • Monitoring and debugging the performance of complex systems that may use a variable number of steps or external API calls
  • Ensuring data quality and security across the different components of the system

3. What are some emerging paradigms and tools to address the challenges of compound AI systems?

  • Composition frameworks and strategies like LangChain, LlamaIndex, and inference techniques like chain-of-thought to help design these systems
  • Optimization tools like DSPy that can tune the components of a compound system end-to-end
  • Cost optimization frameworks like FrugalGPT and AI gateways to route inputs to the most appropriate components
  • LLMOps and DataOps tools to monitor and debug the complex behavior of compound systems
Shared by Daniel Chen ยท
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