超越“四人帮”：现代人工智能系统的实用设计模式

Why Design Patterns for AI Systems: The Gang of Four's object-oriented patterns influenced software dev. Cloud computing introduced new patterns. ML community had ML design patterns. These patterns help solve known problems, speed up dev, reduce errors, and make systems easier to maintain. Modern AI systems bring new challenges not addressed by classic software or ML patterns. Many AI patterns have emerged to build well-architected AI systems, organized into 5 categories.
Prompting and Context Patterns: In modern AI systems, behavior depends on instructions and context. Prompting and context patterns help improve model's reasoning, accuracy, etc. Four specific prompting patterns:
- Few-Shot Prompting Pattern: Provides examples in the prompt to guide the model without re-training. Helps with inconsistent outputs, task ambiguity, etc. Can guide model's output format, adapt to new scenarios, and reduce hallucinations.
- Role Prompting Pattern: Instructs the model to take on a specific persona or role to contextualize its responses. Helps steer output style, tone, and content boundaries. Popular AI assistants use role prompts extensively.
- Chain-of-Thought (CoT) Pattern: Encourages the model to think aloud by breaking the problem down into steps. Helps prevent the model from jumping to pre-learned patterns, improves logical reasoning, makes outputs more interpretable, and reduces hallucinations.
- Retrieval-Augmented Generation (RAG) Pattern: Combines the model's reasoning abilities with real-time access to external knowledge. Helps when the model needs up-to-date information, relies on domain-specific data, or requires accuracy and transparency.
Responsible AI Patterns: Prompting and Context Patterns reduce ambiguity, but additional safeguards are needed. Responsible AI Patterns focus on safety, fairness, and ethical compliance. Output Guardrails Pattern applies rules after the model generates an output to prevent incorrect, biased, or harmful content. Model Critic Pattern uses a dedicated fact-checking model to verify the primary model's output.
User Experience (UX) Patterns: Well-defined UX patterns are essential for handling new types of interactions. Contextual Guidance Pattern provides user guidance to lower the learning curve. Editable Output Pattern allows users to modify generated content for better perception of human-AI collaboration. Iterative Exploration Pattern enables users to quickly iterate and refine outputs.
AI-Ops Patterns: When putting AI software into production, new operational challenges arise. AI-Ops manages performance, cost, and user interactions. Metrics-Driven AI-Ops Pattern tracks various metrics and sets up alerts. Prompt-Model-Config Versioning Pattern manages prompt, model, and configuration changes to prevent regressions. Standard operational best practices like rigorous QA checks, regression testing, etc. are also important.
Optimization Patterns: As AI applications grow, operational bottlenecks occur. Three optimization patterns:
- Prompt Caching Pattern: Caches and reuses responses to reduce LLM calls and latency. Prefix caching is more effective.
- Continuous Dynamic Batching Pattern: Batches incoming requests to maximize GPU utilization and system throughput. Production-ready tools offer solutions.
- Intelligent Model Routing Pattern: Routes queries to appropriate models based on complexity or context. Balances cost-efficiency and model accuracy.
Advanced Patterns: There are several advanced areas not covered in the article:
- Fine-Tuning and Customizing Models: Fine-tuning, customization, and optimizing large foundational models for specific use cases. Platforms enable easy customization.
- Multi-Agent Orchestration: Using multiple specialized AI agents working collaboratively. Common patterns include LLM-as-a-Judge, Role-Based Multi-Agent Collaboration, Reflection Loops, and Tool-Using Agents.
- Agentic AI and Autonomous Systems: Building autonomous AI agents that dynamically plan and execute tasks. A rapidly growing domain with its own emerging best practices.