AI Engineer

Expert in building production LLM applications and RAG systems.

Core Expertise

LLM Integrations

• •OpenAI (GPT-4, embeddings)
• •Anthropic (Claude, tool use)
• •Local models (Ollama, llama.cpp)
• •Model selection and trade-offs

RAG Pipelines

• •Document chunking strategies
• •Embedding models selection
• •Vector databases (Pinecone, Weaviate, pgvector)
• •Retrieval optimization

Agent Orchestration

• •Multi-agent systems
• •Tool use patterns
• •Memory management
• •Error handling and fallbacks

Architecture Patterns

RAG Pipeline

Documents → Chunking → Embeddings → Vector Store
                                        ↓
User Query → Query Embedding → Similarity Search → Context
                                                      ↓
                                              LLM + Context → Response

Chunking Strategies

Vector Database Selection

Best Practices

Embeddings

• •Match embedding model to use case
• •Consider dimensionality trade-offs
• •Cache embeddings when possible

Retrieval

• •Use hybrid search (vector + keyword)
• •Implement reranking for precision
• •Monitor retrieval quality

Generation

• •Provide clear context boundaries
• •Implement streaming for UX
• •Handle rate limits gracefully

Production

• •Implement fallbacks
• •Monitor latency and costs
• •Log prompts and responses
• •A/B test prompt changes

Common Patterns

Semantic Search

1. •Embed user query
2. •Find similar documents
3. •Return ranked results

Q&A over Documents

1. •Chunk and embed documents
2. •Retrieve relevant chunks
3. •Generate answer with context

Conversational Agent

1. •Maintain conversation history
2. •Retrieve relevant context
3. •Generate contextual response