TL;DR

AI system design patterns are reusable solutions to common AI architecture challenges. Master patterns like RAG, chain-of-thought orchestration, and human-in-the-loop to build systems that are reliable, maintainable, and perform well in production.

Why it matters

Building AI features is easy. Building AI systems that work reliably at scale is hard. Design patterns capture lessons learned from production deployments—use them to avoid reinventing solutions and making predictable mistakes.

Core AI design patterns

Pattern 1: Retrieval-Augmented Generation (RAG)

Problem: LLMs have knowledge cutoffs and hallucinate when asked about unknown topics.

Solution: Retrieve relevant context from your data before generating responses.

Architecture:

User Query → Embedding → Vector Search → Retrieved Context
                                            ↓
                           LLM (Query + Context) → Response

When to use:

  • Question answering over your documents
  • Customer support with company knowledge
  • Any task requiring current or proprietary information

Key decisions:

  • Chunk size (too small = missing context, too large = noise)
  • Retrieval count (balance relevance vs. token limits)
  • Embedding model selection
  • Re-ranking strategy

Pattern 2: Chain-of-Thought Orchestration

Problem: Complex tasks fail when handled in a single prompt.

Solution: Break tasks into steps, each with focused prompts and validation.

Architecture:

Input → Step 1 (Analyze) → Step 2 (Plan) → Step 3 (Execute) → Step 4 (Validate) → Output
          ↓                  ↓                ↓                  ↓
       [Validate]         [Validate]      [Validate]         [Validate]

When to use:

  • Multi-step reasoning tasks
  • Tasks requiring planning before execution
  • Complex code generation
  • Document analysis and synthesis

Key decisions:

  • How many steps to decompose into
  • What to validate between steps
  • How to handle step failures
  • Whether steps can run in parallel

Pattern 3: Human-in-the-Loop

Problem: AI makes mistakes that require human judgment to catch.

Solution: Route uncertain or high-stakes decisions to humans.

Architecture:

Input → AI Processing → Confidence Check
                            ↓
            High confidence: Auto-approve
            Low confidence:  Human Review → Feedback Loop

When to use:

  • High-stakes decisions (financial, medical, legal)
  • Content moderation
  • Training data generation
  • Any task where AI errors have significant consequences

Key decisions:

  • Confidence thresholds for routing
  • Queue management for human reviewers
  • How to incorporate feedback
  • Escalation procedures

Pattern 4: Model Router

Problem: Different tasks require different models (cost, capability, speed tradeoffs).

Solution: Route requests to appropriate models based on task characteristics.

Architecture:

Input → Classifier → Simple task: Fast/cheap model
                  → Complex task: Capable/expensive model
                  → Specialized task: Domain model

When to use:

  • Mixed workloads with varying complexity
  • Cost optimization at scale
  • When you need specialized models for some tasks

Key decisions:

  • Routing criteria (cost, latency, capability)
  • Classifier accuracy requirements
  • Fallback strategies
  • Monitoring and adjustment

Pattern 5: Guardrails Pattern

Problem: AI outputs need to comply with policies and constraints.

Solution: Wrap AI with input/output validation layers.

Architecture:

Input → Input Guards → AI Processing → Output Guards → Response
            ↓                              ↓
        [Reject/Modify]              [Filter/Reject]

When to use:

  • Any customer-facing AI application
  • Regulated industries
  • When content policies must be enforced

Key decisions:

  • What to guard against
  • Hard blocks vs. soft warnings
  • How to communicate rejections
  • Logging and monitoring

Advanced patterns

Multi-Agent Systems

Multiple AI agents collaborate on complex tasks:

Specialized agents:

  • Researcher agent: Gathers information
  • Planner agent: Creates action plans
  • Executor agent: Carries out tasks
  • Critic agent: Reviews and improves output

Coordination patterns:

  • Sequential: Agents pass work in order
  • Parallel: Agents work simultaneously
  • Hierarchical: Manager agent coordinates specialists

Caching and Memoization

Reduce costs and latency by reusing results:

Cache strategies:

  • Exact match: Cache identical queries
  • Semantic similarity: Cache similar queries
  • Embedding cache: Store and reuse embeddings
  • Partial cache: Cache intermediate results

Cache invalidation:

  • Time-based expiration
  • Event-driven invalidation
  • Manual refresh triggers

Fallback and Redundancy

Handle failures gracefully:

Fallback strategies:

  • Primary → Secondary model
  • AI → Rule-based fallback
  • Expensive → Cheap model degradation
  • Cached response → Stale but available

Pattern selection guide

Scenario Primary pattern Supporting patterns
Customer Q&A RAG Guardrails, Caching
Content generation Chain-of-thought Human-in-loop, Guardrails
High-volume simple tasks Model Router Caching
Complex analysis Multi-agent Chain-of-thought
Regulated industry Human-in-loop Guardrails

Implementation considerations

Observability

Every pattern needs monitoring:

  • Request/response logging (without sensitive data)
  • Latency tracking per component
  • Error rates and types
  • Cost attribution
  • Quality metrics

Testing strategies

  • Unit test individual components
  • Integration test pattern flows
  • Load test for scale patterns
  • Red team for guardrails
  • A/B test for optimization

Evolution and maintenance

Patterns aren't static:

  • Monitor pattern effectiveness
  • Adjust thresholds based on data
  • Update as models improve
  • Retire patterns when obsolete

Common mistakes

Mistake Impact Better approach
Over-engineering early Wasted effort, complexity Start simple, add patterns as needed
No fallbacks System fails completely Always have degraded modes
Ignoring costs Budget overruns Instrument and optimize
Tight coupling Hard to evolve Design for component replacement
No monitoring Blind to problems Observe everything

What's next

Dive deeper into AI architecture: