Bifocal Perspective of Compound AI Systems using Gemini 1.5 Pro 👓

I recently came across a research publication titled The Shift from Models to Compound AI Systems on the Berkeley Artificial Intelligence Research (BAIR) Lab's website. Being on a time crunch I wanted a quick gist of the blog post without getting into the nitty-gritties. To make my work easier, I leveraged the capabilities of Google's Gemini 1.5 Pro to help me in this process of summarization. Once I got a rudimentary idea of the topic, I dived deep into it, and undoubtedly BAIR has done a great job with this 👏🏻

Why use Google's Gemini 1.5 Pro for summarizing a research publication as opposed to Gemini 1.0 Pro?

Gemini 1.5 Pro offers distinct advantages over Gemini 1.0 Pro for summarizing research publications:

  • Vastly Expanded Context Window: Gemini 1.5 Pro can process up to 1 million tokens (the units that makeup text, code, etc.), compared to Gemini 1.0 Pro's limit of 32,000. This means it can understand and retain far more information about a research paper, enhancing its ability to produce accurate summaries.
  • Superior Reasoning Across Modalities: Gemini 1.5 Pro excels at understanding and connecting information across different forms (text, tables, figures). This is crucial for research papers, which frequently combine these elements for a complete understanding.
  • Performance Improvements: Benchmarks demonstrate that Gemini 1.5 Pro outperforms 1.0 Pro on key metrics for summarization, including accuracy, conciseness, and handling of complex terminology.
  • Time Savings: Gemini 1.5 Pro can handle vast amounts of data quickly. For lengthy or dense research publications, this translates into significantly faster summarization times, freeing up researchers for deeper analysis.
  • Reduced Errors: Because of its large context window and improved understanding, summaries generated by Gemini 1.5 Pro are less likely to contain misinterpretations of the original research, increasing user confidence.
  • Focus on Key Insights: Gemini 1.5 Pro's more reliable and accurate summaries let researchers quickly pinpoint the crucial findings of a paper, leading to a more streamlined and efficient workflow.

  • Before you proceed, here are a few things that should be kept in mind!

  • Availability: Gemini 1.5 Pro is a newer model and might be in a more limited release than Gemini 1.0 Pro. Ensure you have access to it before committing to its use.
  • Complexity: If the research papers you're summarizing are relatively short and straightforward, the added capability of Gemini 1.5 Pro might be less of a necessity.

  • Gemini 1.5 Pro revolutionizes research publication summarization with a million-token capacity, adept cross-modal reasoning, and superior performance metrics, promising accelerated summarization, reduced errors, and enhanced insight extraction for researchers!

    V1 - The Shift from Models to Compound AI Systems

    Imagine you're building a complex machine. Instead of using just one type of tool, you combine different tools with unique capabilities to achieve the best results. This is the idea behind compound AI systems, which are becoming the new frontier in AI. Instead of relying on a single AI model, researchers and developers are increasingly combining multiple models and components to tackle complex tasks.

    AI systems are becoming more sophisticated, moving beyond individual models to interconnected systems called Compound AI Systems (CAS). These systems combine different components, such as language models, search engines, and specialized tools, to achieve better performance, handle dynamic data, and offer more control and trust.

    Better performance: By combining different strengths, compound systems can outperform single models on specific tasks. Think of it like a team of specialists working together, each contributing their expertise.

    Adaptability: These systems can access and process real-time information, making them more adaptable to changing situations. This is crucial for applications like search engines and personal assistants that need to stay up-to-date.

    Increased trust: By incorporating mechanisms to verify information and explain their reasoning, compound systems can build trust with users. This is especially important in areas like healthcare and finance, where decisions have significant consequences.

    Cost-effectiveness: Developers can choose the right combination of models and components to meet specific needs and budgets, making AI solutions more accessible and efficient.


    Design complexity: Choosing the right components and figuring out how to best combine them is a complex task. It's like figuring out the best way to assemble a team of specialists and ensure they work well together.

    Optimization difficulties: Optimizing the entire system for a specific goal can be tricky, as different components may require different approaches. It's like finding the best way to train and coordinate a team of specialists with diverse skills.

    Operational challenges: Monitoring, debugging, and securing these complex systems require new tools and approaches. It's like managing a team of specialists and ensuring they work efficiently and securely.


    Frameworks and Tools: Developers can leverage frameworks and tools to design and build CAS more easily.

    Automatic Optimization: Techniques like DSPy enable end-to-end optimization of CAS pipelines, including non-differentiable components.

    Cost Optimization: Tools like FrugalGPT help optimize the allocation of resources to achieve the best results within budget constraints.


    Creating realistic and engaging content: Imagine AI systems that can write captivating stories, design stunning visuals, and even compose music.

    Accelerating scientific discovery: AI could help solve complex scientific problems and lead to breakthroughs in medicine, materials science, and other fields.

    Improving healthcare: AI could assist doctors in diagnosing diseases, personalizing treatment plans, and developing new drugs.

    Making smarter financial decisions: AI could help investors make informed decisions and manage financial risks more effectively.

    Providing better customer service: AI-powered customer service agents could understand customer needs and provide personalized and efficient support.


    While challenges remain, compound AI systems hold immense potential to revolutionize various fields. As research and development continue, we can expect these systems to become increasingly powerful and accessible, leading to a future where AI plays an even more significant role in our lives.

    V2 - The Shift from Models to Compound AI Systems

    Generative AI models like LLMs have gained immense popularity, initially leading to a focus on models as the primary ingredient in AI application development. However, there is a growing shift towards Compound AI Systems (CAS), which combine multiple components, including models, retrievers, and external tools, to achieve state-of-the-art AI results. This paper argues that compound AI systems, which combine multiple AI models and components, are becoming the dominant paradigm for achieving state-of-the-art results in AI, even as individual models continue to improve. This trend is driven by several factors:

    Superior performance: Compound systems can outperform monolithic models on specific tasks by leveraging specialized components and sophisticated system design. For example, AlphaCode 2 surpassed human performance in coding competitions by using multiple LLMs and filtering strategies, while AlphaGeometry combined an LLM with a symbolic solver to excel at olympiad problems.

    Dynamic adaptation: Compound systems can incorporate real-time data and adapt to changing conditions, overcoming the limitations of static datasets used for training individual models. This enables applications like search engines and personalized assistants to provide up-to-date information.

    Enhanced control and trust: By incorporating filtering, verification, and explanation mechanisms, compound systems can offer greater control over AI behavior and increase user trust. This is crucial for applications in sensitive domains like healthcare and finance.

    Flexible performance-cost trade-off: Compound systems allow developers to adjust the complexity and cost of their applications by selecting appropriate models and components. This enables efficient resource allocation and caters to diverse user needs and budgets.


    Vast design space: The numerous choices for models, components, and system architectures create a complex design space that is difficult to navigate.

    Optimization challenges: Optimizing the entire system for a specific goal can be difficult due to the presence of non-differentiable components and the need for co-optimization of individual modules.

    Operational complexity: Monitoring, debugging, and ensuring the security of compound systems require new tools and approaches compared to managing individual models.


    Composition frameworks and strategies: Tools like LangChain and LlamaIndex enable developers to build applications by composing calls to various models and components. Additionally, research on inference strategies like chain-of-thought and self-consistency helps improve system outputs.

    Automatic optimization: DSPy offers a framework for automatically optimizing compound systems by tuning prompts, examples, and parameters to maximize a target metric. This allows for end-to-end optimization similar to training neural networks.

    Cost optimization: FrugalGPT and AI Gateways dynamically route inputs to different models based on cost and performance considerations, enabling efficient resource allocation and cost reduction.

    LLMOps and DataOps: Tools like LangSmith and Databricks Inference Tables facilitate monitoring and debugging of complex AI systems by tracking intermediate outputs and data pipeline quality. Research efforts like DSPy Assertions and AI-based quality evaluation methods aim to further automate and improve the operational aspects of compound systems.


    Content creation: Generating realistic and creative content like text, images, and videos.

    Scientific discovery: Solving complex scientific problems and accelerating research.

    Healthcare: Diagnosing diseases, personalizing treatment plans, and developing new drugs.

    Finance: Making informed investment decisions and managing financial risks.

    Customer service: Providing personalized and efficient customer support.


    Overall, this paper emphasizes the growing importance of compound AI systems in achieving advanced AI capabilities. While challenges remain in designing, optimizing, and operating these systems, ongoing research and development of new tools and paradigms are paving the way for their wider adoption and transformative impact across various domains.

    Do you see any difference in the way the two summaries are presented?

    How are the two different?

    One of them is a more technical summary as compared to the other, and they differ in the following aspects:


  • Target audience: GenAI engineers with a strong understanding of AI concepts and terminology.
  • Level of detail: Highly technical, focusing on specific methodologies, algorithms, and research efforts. Includes evidence backed by numbers and detailed descriptions of system designs.
  • Purpose: To provide a comprehensive overview of the research and its implications for engineers working in the field of GenAI.

  • Non-Technical

  • Target audience: General audience with limited technical knowledge of AI.
  • Level of detail: Less technical, using analogies and simpler language to explain the key concepts and potential applications of compound AI systems. Avoids technical jargon and focuses on the overall impact and potential benefits.
  • Purpose: To introduce the concept of compound AI systems to a broader audience and raise awareness of their potential impact on various domains.

  • In essence, the technical summary is geared towards experts in the field, providing in-depth analysis and technical details. The non-technical summary aims to make the same information accessible to a wider audience by simplifying the language and focusing on the broader implications and potential applications.

    Can you identify which of the two is more technical as compared to the other?

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    Written on February 21, 2024