Beyond LLMs: Compound Systems, Agents, and Building AI Products

Author(s): ifttt-user

 

TL;DR: Building successful AI products requires more than just deploying large language models (LLMs); it necessitates integrating AI models with components like data pipelines, retrieval systems, agents, and user interfaces to create holistic solutions that meet user needs. By applying frameworks like the Whole Product Model, developers can differentiate their products and gain a competitive edge, as illustrated by examples like Uber’s Michelangelo platform and OpenAI’s ChatGPT.

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Building AI Products Effectively

In the rapidly evolving landscape of artificial intelligence (AI), creating successful AI products extends beyond deploying the latest large language models (LLMs). It requires a holistic approach that integrates technology, user needs, and strategic differentiation. Drawing inspiration from frameworks like Maslow’s hierarchy of needs and Geoffrey Moore’s “Crossing the Chasm,” this article explores how to build AI products that not only leverage cutting-edge technology but also deliver comprehensive value to users.

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Understanding the Whole Product Model

Geoffrey Moore’s “Simplified Whole Product Model,” adapted from Theodore Levitt’s original concept, emphasizes that a product must address the complete spectrum of customer needs—not just its core functionality. In Moore’s model, the product is envisioned in layers:

• Generic Product: The basic version offering core functionalities.
• Expected Product: The minimal features customers anticipate.
• Augmented Product: Additional features and services that differentiate the product.
• Potential Product: Future enhancements that could further satisfy customer needs.

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Adapting the Model for AI Products

In the context of AI, especially with the rise of LLMs, we can adapt the Whole Product Model to better represent the complexities involved:

1. Core Product (AI Model): The foundational AI technology, such as an LLM or a specialized algorithm.
2. Whole Product (Enablers): Complementary components that make the AI model usable and valuable, including data pipelines, user interfaces, and integration capabilities.
3. Differentiated Product: Unique features or services that set the AI product apart in a competitive market, such as proprietary data, specialized tools, or community support.

Customizing for Different User Segments

Different user segments have varying needs and constraints:

• Enterprise Clients: Prioritize security, compliance, and scalability.
• Developers: Focus on integration capabilities, customization, and tool support.
• End Consumers: Value ease of use, reliability, and seamless experiences.

Recognizing these differences allows AI product developers to tailor enablers and differentiators accordingly.

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Key Components in Building AI Applications

To construct a successful AI application, several critical components must be integrated:

1. Language Models (LLMs and SLMs)

• Large Language Models (LLMs): Extensive models trained on vast datasets, capable of generating coherent and contextually rich text across domains (e.g., GPT-4).
• Small Language Models (SLMs): More compact models tailored for specific tasks, offering advantages in deployment simplicity and cost-effectiveness.

Considerations in Choosing Models:

• Performance vs. Cost: Larger models may offer better performance but at higher computational costs.
• Privacy Concerns: SLMs can be deployed on-premises, enhancing data privacy.
• Evaluation Complexity: LLMs may introduce variability, making testing and validation more challenging.

2. Retrieval-Augmented Generation (RAG)

RAG combines the strengths of information retrieval and language generation. By integrating external data sources, the model can generate more accurate and up-to-date responses.

Key Aspects of RAG:

• Data Retrieval: Efficient mechanisms to fetch relevant information from databases or the web.
• Context Integration: Merging retrieved data into the model’s input to enhance response quality.
• Trade-offs with Fine-Tuning: Deciding when to use RAG versus fine-tuning models depends on factors like data freshness, specificity, and computational resources.

3. Agents and Agentic Behavior

• Agents: Software entities capable of autonomous actions to achieve specified goals, often interacting with their environment and other systems.
• Agentic Behavior: The ability of agents to operate independently, make decisions, and utilize tools or APIs.

Agentic vs. Agentless Systems:

• Agentic Systems: Offer flexibility and adaptability but may introduce unpredictability.
• Agentless (Flow-Engineered) Systems: Rely on deterministic workflows, enhancing reliability and explainability.

4. Supporting Components

• Data Pipelines: Systems for data acquisition, processing, and transformation.
• Knowledge Bases: Structured repositories that provide context and factual information.
• User Interfaces (UI): Platforms for user interaction, such as web apps or chat interfaces.
• Infrastructure and Operations (Ops): Considerations for scalability, deployment, monitoring, and security.
• Observability: Tools for logging, monitoring, and tracing to ensure system reliability.

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Compound AI Systems

Compound AI systems are architectures where multiple AI components interact to perform complex tasks. They integrate models, retrieval mechanisms, agents, and tools into a cohesive system.

Design Considerations:

• Control Logic: Determining whether traditional programming or AI-driven agents manage the system’s workflow.
• Resource Allocation: Balancing computational resources among components for optimal performance.
• Optimization: Ensuring that interactions between components enhance overall system efficiency and effectiveness.

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From Compound Systems to Whole AI Products

By mapping compound AI systems to the adapted Whole Product Model, we can better understand how technical components translate into user value.

Incorporating Constraints

• Performance Requirements: Speed, accuracy, and scalability needs.
• Regulatory Compliance: Adhering to laws and standards, especially in data-sensitive applications.
• User Expectations: Meeting or exceeding the features and usability that customers anticipate.

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Defensibility and Building Moats in AI

In a competitive market, AI products must establish defensibility. Strategies include:

• Community Engagement:
  • Building a strong user community fosters loyalty and provides valuable feedback.
• Specialization:
  • Focusing on niche markets or specific problems where the product can excel.
• Proprietary Data and Models:
  • Leveraging unique datasets or algorithms that competitors cannot easily replicate.
• Integration and Ecosystem Building:
  • Forming partnerships and integrating with other platforms to enhance value.

Adding the Differentiated Product Layer

This layer represents the unique aspects that distinguish an AI product:

• Innovation at the Application Layer: Developing novel features or user experiences.
• Strategic Partnerships: Collaborations that provide competitive advantages.
• Unique Data Assets: Exclusive access to data that improves model performance.
• Brand and Reputation: Building trust through reliability and ethical practices.

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Case Studies: Applying the Framework

1. Uber’s Michelangelo Platform

Challenge:

• Managing diverse machine learning needs across different services, such as ride matching, ETA predictions, and fraud detection.

Solution:

• Core Product: An internal AI platform that supports data processing, model training, deployment, and monitoring.
• Enablers:
  • Data Pipeline: Systems like Palette for feature management.
  • Tools: Michelangelo Studio for workflow management.
  • Ops: Scalable infrastructure and monitoring systems.
• Differentiation:
  • Scale and Efficiency: Optimized for Uber’s global operations.
  • Developer Experience: Tools that enhance productivity.
  • Integration: Deep integration with Uber’s services and data.

2. OpenAI’s ChatGPT

Challenge:

• Providing accessible AI assistance for a wide range of tasks to users worldwide.

Solution:

• Core Product: Advanced language models (e.g., GPT-4).
• Enablers:
  • User Interface: Web and mobile apps for interaction.
  • Ops: Scalable infrastructure to handle high demand.
  • Safety Measures: Systems to monitor and guide model outputs.
• Differentiation:
  • Continuous Improvement: Regular model updates.
  • Community Engagement: Active feedback mechanisms.
  • Ecosystem Development: Plugins and integrations.

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Aligning with the Market Development Life Cycle

Adapting Moore’s Market Development Life Cycle involves:

• Innovators: Early adopters who engage with the core product (e.g., beta testers).
• Early Majority: Users who require the whole product with necessary enablers.
• Late Majority and Laggards: Users who adopt once the product is fully mature and widely accepted.

Successfully transitioning through these stages requires adding layers of value and addressing broader user needs.

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Conclusion

Building AI products effectively demands a holistic approach that integrates advanced technologies with user-centric design and strategic differentiation. By employing the adapted Whole Product Model, developers can ensure that they not only meet but exceed customer expectations, creating sustainable competitive advantages in the AI market.

Crafted using generative AI from insights found on Towards AI.

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