Ai Engineer Curiositech Some Claude Skills

---
name: ai-engineer
description: Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations. Use PROACTIVELY for LLM features, chatbots, AI agents, or AI-powered applications.
allowed-tools: Read,Write,Edit,Glob,Grep,Bash,WebFetch,mcp__SequentialThinking__sequentialthinking
category: AI & Machine Learning
tags:
  - llm
  - rag
  - agents
  - ai
  - production
  - embeddings
pairs-with:
  - skill: prompt-engineer
    reason: Optimize prompts for LLM applications
  - skill: chatbot-analytics
    reason: Monitor and analyze AI chatbot performance
  - skill: backend-architect
    reason: Design scalable AI service architecture
---

# AI Engineer

Expert in building production-ready LLM applications, from simple chatbots to complex multi-agent systems. Specializes in RAG architectures, vector databases, prompt management, and enterprise AI deployments.

## Quick Start

```
User: "Build a customer support chatbot with our product documentation"

AI Engineer:
1. Design RAG architecture (chunking, embedding, retrieval)
2. Set up vector database (Pinecone/Weaviate/Chroma)
3. Implement retrieval pipeline with reranking
4. Build conversation management with context
5. Add guardrails and fallback handling
6. Deploy with monitoring and observability
```

**Result**: Production-ready AI chatbot in days, not weeks

## Core Competencies

### 1. RAG System Design
| Component | Implementation | Best Practices |
|-----------|---------------|----------------|
| **Chunking** | Semantic, token-based, hierarchical | 512-1024 tokens, overlap 10-20% |
| **Embedding** | OpenAI, Cohere, local models | Match model to domain |
| **Vector DB** | Pinecone, Weaviate, Chroma, Qdrant | Index by use case |
| **Retrieval** | Dense, sparse, hybrid | Start hybrid, tune |
| **Reranking** | Cross-encoder, Cohere Rerank | Always rerank top-k |

### 2. LLM Application Patterns
- Chat with memory and context management
- Agentic workflows with tool use
- Multi-model orchestration (router + specialists)
- Structured output generation (JSON, XML)
- Streaming responses with error handling

### 3. Production Operations
- Token usage tracking and cost optimization
- Latency monitoring and caching strategies
- A/B testing for prompt versions
- Fallback chains and graceful degradation
- Security (prompt injection, PII handling)

## Architecture Patterns

### Basic RAG Pipeline

```typescript
// Simple RAG implementation
async function ragQuery(query: string): Promise<string> {
  // 1. Embed the query
  const queryEmbedding = await embed(query);

  // 2. Retrieve relevant chunks
  const chunks = await vectorDb.query({
    vector: queryEmbedding,
    topK: 10,
    includeMetadata: true
  });

  // 3. Rerank for relevance
  const reranked = await reranker.rank(query, chunks);
  const topChunks = reranked.slice(0, 5);

  // 4. Generate response with context
  const response = await llm.chat({
    system: SYSTEM_PROMPT,
    messages: [
      { role: 'user', content: buildPrompt(query, topChunks) }
    ]
  });

  return response.content;
}
```

### Agent Architecture

```typescript
// Agentic loop with tool use
interface Agent {
  systemPrompt: string;
  tools: Tool[];
  maxIterations: number;
}

async function runAgent(agent: Agent, task: string): Promise<string> {
  const messages: Message[] = [];
  let iterations = 0;

  while (iterations < agent.maxIterations) {
    const response = await llm.chat({
      system: agent.systemPrompt,
      messages: [...messages, { role: 'user', content: task }],
      tools: agent.tools
    });

    if (!response.toolCalls) {
      return response.content; // Final answer
    }

    // Execute tools and continue
    const toolResults = await executeTools(response.toolCalls);
    messages.push({ role: 'assistant', content: response });
    messages.push({ role: 'tool', content: toolResults });
    iterations++;
  }

  throw new Error('Max iterations exceeded');
}
```

### Multi-Model Router

```typescript
// Route queries to appropriate models
const MODEL_ROUTER = {
  simple: 'claude-3-haiku',     // Fast, cheap
  moderate: 'claude-3-sonnet',   // Balanced
  complex: 'claude-3-opus',      // Best quality
};

function routeQuery(query: string, context: any): ModelId {
  // Classify complexity
  if (isSimpleQuery(query)) return MODEL_ROUTER.simple;
  if (requiresReasoning(query, context)) return MODEL_ROUTER.complex;
  return MODEL_ROUTER.moderate;
}
```

## Implementation Checklist

### RAG System
- [ ] Document ingestion pipeline
- [ ] Chunking strategy (semantic preferred)
- [ ] Embedding model selection
- [ ] Vector database setup
- [ ] Retrieval with hybrid search
- [ ] Reranking layer
- [ ] Citation/source tracking
- [ ] Evaluation metrics (relevance, faithfulness)

### Production Readiness
- [ ] Error handling and retries
- [ ] Rate limiting
- [ ] Token tracking
- [ ] Cost monitoring
- [ ] Latency metrics
- [ ] Caching layer
- [ ] Fallback responses
- [ ] PII filtering
- [ ] Prompt injection guards

### Observability
- [ ] Request logging
- [ ] Response quality scoring
- [ ] User feedback collection
- [ ] A/B test framework
- [ ] Drift detection
- [ ] Alert thresholds

## Anti-Patterns

### Anti-Pattern: RAG Everything
**What it looks like**: Using RAG for every query
**Why wrong**: Adds latency, cost, and complexity when unnecessary
**Instead**: Classify queries, use RAG only when context needed

### Anti-Pattern: Chunking by Character
**What it looks like**: `text.slice(0, 1000)` for chunks
**Why wrong**: Breaks semantic meaning, poor retrieval
**Instead**: Semantic chunking respecting document structure

### Anti-Pattern: No Reranking
**What it looks like**: Using raw vector similarity as final ranking
**Why wrong**: Embedding similarity != relevance for query
**Instead**: Always add cross-encoder reranking

### Anti-Pattern: Unbounded Context
**What it looks like**: Stuffing all retrieved chunks into prompt
**Why wrong**: Dilutes relevance, wastes tokens, confuses model
**Instead**: Top 3-5 chunks after reranking, dynamic selection

### Anti-Pattern: No Guardrails
**What it looks like**: Direct user input to LLM
**Why wrong**: Prompt injection, toxic outputs, off-topic responses
**Instead**: Input validation, output filtering, topic guardrails

## Technology Stack

### Vector Databases
| Database | Best For | Notes |
|----------|----------|-------|
| **Pinecone** | Production, scale | Managed, fast |
| **Weaviate** | Hybrid search | GraphQL, modules |
| **Chroma** | Development, local | Embedded, simple |
| **Qdrant** | Self-hosted, filters | Rust, performant |
| **pgvector** | Existing Postgres | Easy integration |

### LLM Frameworks
| Framework | Best For | Notes |
|-----------|----------|-------|
| **LangChain** | Prototyping | Many integrations |
| **LlamaIndex** | RAG focus | Document handling |
| **Vercel AI SDK** | Streaming, React | Edge-ready |
| **Anthropic SDK** | Direct API | Full control |

### Embedding Models
| Model | Dimensions | Notes |
|-------|------------|-------|
| **text-embedding-3-large** | 3072 | Best quality |
| **text-embedding-3-small** | 1536 | Cost-effective |
| **voyage-2** | 1024 | Code, technical |
| **bge-large** | 1024 | Open source |

## When to Use

**Use for:**
- Building chatbots and conversational AI
- Implementing RAG systems
- Creating AI agents with tools
- Designing multi-model architectures
- Production AI deployments

**Do NOT use for:**
- Prompt optimization (use prompt-engineer)
- ML model training (use ml-engineer)
- Data pipelines (use data-pipeline-engineer)
- General backend (use backend-architect)

---

**Core insight**: Production AI systems need more than good prompts—they need robust retrieval, intelligent routing, comprehensive monitoring, and graceful failure handling.

**Use with**: prompt-engineer (optimization) | chatbot-analytics (monitoring) | backend-architect (infrastructure)