Design Patterns for Compound AI Systems
Compound AI systems are transforming how intelligent systems are built, shifting focus from standalone models to dynamic pipelines of interconnected modules. These systems integrate components like reasoning, planning, and retrieval to create sophisticated solutions tailored to diverse applications. Unlike traditional AI, compound systems excel in modularity, enabling seamless collaboration among components.
Key Deployment Patterns
Common patterns include RAG systems for advanced query understanding, multi-agent solvers for collaborative problem-solving, and Co-Pilots for human-in-the-loop assistance. Each pattern leverages specific modules like retrievers, generators, and planners to address complex workflows.
“Compound AI systems are the building blocks of modern intelligence, where modules collaborate to deliver solutions beyond the capabilities of standalone models.”
What are Compound AI Systems?
Compound AI systems are intelligent frameworks composed of interconnected modules that perform specific tasks. Unlike standalone models, these systems rely on components like retrieval, reasoning, and planning to build complex pipelines tailored to dynamic requirements.
Common Deployment Patterns
- RAG (Retrieval and Generation): Systems focused on understanding user queries, leveraging thought generation, reasoning, and contextual data to deliver advanced responses, ideal for applications like conversational AI and co-pilots.
- Multi-Agent Problem Solvers: Collaborative systems where agents assume specific roles and interact to solve complex problems. Each agent accesses specialized tools to reason, plan, and execute tasks.
- Conversational AI: Dialogue-driven systems like customer service agents that utilize conversational memory and dialogue generation to provide a human-like interaction experience.
- Co-Pilots: Systems designed for human-in-the-loop interactions, equipped with tools, data, and planning capabilities to assist users in specific environments, such as research or project management.
Key Components of Compound AI Systems
Compound AI systems consist of interconnected modules like generators, retrievers, rankers, and classifiers. These modules rely on core functionalities such as:
- Reasoning: Logical problem-solving methods using data observations and hypothesis validation.
- Thought: Coherent cause-effect generation based on reasoning.
- Chain of Thought: Breaking solutions into logically connected steps for clarity and efficiency.
- Planning: Decision-making skills to achieve sub-goals and navigate from current to desired states.
- Tools: Sub-modules that enable interaction with external environments.
- Action: Decisive steps taken by agents to achieve goals, often invoking tools.
- Environment: The external context where actions and rewards occur, such as applications, simulations, or real-world scenarios.
Tags:
Agents, LLM, AI, GPT
