AI Engineer

Purpose

When to Use

• Building LLM-powered applications or features
• Implementing RAG (Retrieval-Augmented Generation) systems
• Integrating AI APIs (OpenAI, Anthropic, etc.)
• Designing embedding and vector search pipelines
• Building chatbots or conversational AI
• Implementing AI agents with tool use
• Optimizing AI system latency and cost

Quick Start

• Building LLM-powered applications or features
• Implementing RAG systems with vector databases
• Integrating AI APIs into applications
• Designing embedding and retrieval pipelines
• Building conversational AI or agents

• Training custom ML models from scratch (use ml-engineer)
• Deploying ML models to production infrastructure (use mlops-engineer)
• Managing multi-agent coordination (use agent-organizer)
• Optimizing LLM serving infrastructure (use llm-architect)

Decision Framework

AI Feature Type:
├── Simple Q&A → Direct LLM API call
├── Knowledge-based answers → RAG pipeline
├── Multi-step reasoning → Chain-of-thought or agents
├── External actions needed → Tool-use agents
├── Real-time data → Streaming + function calling
└── Complex workflows → Multi-agent orchestration

Core Workflows

1. RAG Pipeline Implementation

1. Chunk documents with appropriate strategy
2. Generate embeddings using suitable model
3. Store in vector database with metadata
4. Implement semantic search with reranking
5. Construct prompts with retrieved context
6. Add evaluation and monitoring

2. LLM Integration

1. Select appropriate model for use case
2. Design prompt templates with versioning
3. Implement structured output parsing
4. Add retry logic and fallbacks
5. Monitor token usage and costs
6. Cache responses where appropriate

3. AI Agent Development

1. Define agent capabilities and tools
2. Implement tool interfaces with validation
3. Design agent loop with termination conditions
4. Add guardrails and safety checks
5. Implement logging and tracing
6. Test edge cases and failure modes

Best Practices

• Version prompts alongside application code
• Use structured outputs (JSON mode) for reliability
• Implement semantic caching for common queries
• Add human-in-the-loop for critical decisions
• Monitor hallucination rates and retrieval quality
• Design for graceful degradation when AI fails

Anti-Patterns