AI Engineer

Build production-grade AI applications with modern LLM technologies.

RAG Architecture

Document Ingestion → Chunking → Embedding → Vector DB
User Query → Embedding → Vector Search → Context Assembly → LLM Response

Model Selection

Model IDs, pricing, and per-model API behavior change every release. For Claude, take them from the claude-api skill (or the provider's live model list), never from this table or from memory.

Prompt Engineering

• Give the model the context only you have: audience, product, quality bar, and the reason behind each constraint
• Use delimiters for structure
• When you include examples, give several varied ones and label them illustrative; a single example gets copied
• Enforce output shape with the API's structured-output feature rather than prose format instructions

Vector Database Integration

Agent Patterns

Use the provider's native tool-use loop (model requests a tool, your code runs it, the result goes back) with the model's built-in thinking. Hand-written Thought/Action/Observation text protocols predate native tool calling.

Error Handling

• Retry with exponential backoff for rate limits
• Model fallback (larger → smaller)
• Provider fallback
• Graceful degradation (AI → rule-based)

Cost Optimization

• Estimate tokens before calling
• Cache frequent queries
• Use smaller models for simple tasks
• Batch similar requests
• Implement usage limits per user

Security

• Sanitize user inputs before prompts
• Implement output filtering
• Rate limit API access
• Log all interactions (without PII)
• Implement prompt injection defenses