- name
- ai-engineer
- description
- >-
AI Engineer
Build practical AI systems that work in production. Data-driven, systematic, performance-focused.
Core Capabilities
- LLM Integration: OpenAI, Anthropic, local models (Ollama, llama.cpp), LiteLLM
- RAG Systems: Chunking, embeddings, vector search, retrieval, re-ranking
- Vector DBs: Chroma (local), Pinecone (managed), Weaviate, FAISS, Qdrant
- Agents & Tools: Tool-calling, multi-step agents, OpenClaw sub-agents
- Data Pipelines: Ingestion, cleaning, transformation, feature engineering
- MLOps: Model versioning (MLflow), monitoring, drift detection, A/B testing
- Evaluation: Benchmark construction, bias testing, performance metrics
Decision Framework
Which LLM provider?
- Prototyping/speed: OpenAI GPT-4o or Anthropic Claude Sonnet
- Local/private: Ollama + Qwen 2.5 32B or Llama 3.3 70B
- Multi-provider abstraction: LiteLLM (swap models without code changes)
- Embeddings: text-embedding-3-small (OpenAI) or nomic-embed-text (local)
Which vector DB?
- Local/dev: Chroma (zero setup)
- Production managed: Pinecone
- Self-hosted production: Qdrant or Weaviate
- Already in Postgres: pgvector extension
RAG or fine-tuning?
- RAG first — always try RAG before fine-tuning. 90% of cases RAG is enough.
- Fine-tune only when: style/tone change needed, domain vocab is highly specialized, latency must be minimal
RAG Workflow
1. Ingest
# Chunk documents (rule of thumb: 512 tokens, 50 overlap)
from langchain.text_splitter import RecursiveCharacterTextSplitter
splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=50)
chunks = splitter.split_documents(docs)2. Embed + store
import chromadb
from chromadb.utils.embedding_functions import OpenAIEmbeddingFunction
client = chromadb.PersistentClient(path="./chroma_db")
ef = OpenAIEmbeddingFunction(api_key=os.environ["OPENAI_API_KEY"], model_name="text-embedding-3-small")
collection = client.get_or_create_collection("docs", embedding_function=ef)
collection.add(documents=[c.page_content for c in chunks], ids=[str(i) for i in range(len(chunks))])3. Retrieve + generate
results = collection.query(query_texts=[user_query], n_results=5)
context = "\
\
".join(results["documents"][0])
response = client.chat.completions.create(
model="gpt-4o",
messages=[
{"role": "system", "content": f"Answer based on this context:\
{context}"},
{"role": "user", "content": user_query},
]
)See references/rag-patterns.md for advanced patterns: re-ranking, hybrid search, HyDE, eval.
LLM Tool Calling (Agents)
tools = [{
"type": "function",
"function": {
"name": "search_docs",
"description": "Search internal documentation",
"parameters": {
"type": "object",
"properties": {"query": {"type": "string"}},
"required": ["query"]
}
}
}]
response = openai.chat.completions.create(model="gpt-4o", messages=messages, tools=tools)See references/agent-patterns.md for multi-step agent loops, error handling, tool schemas.
Critical Rules
- Evaluate early — build an eval set before you build the system
- RAG before fine-tuning — always
- Log everything — prompts, completions, latency, token usage from day one
- Test for bias — especially for user-facing classification or scoring systems
- Never hardcode API keys — use env vars or secret managers
References
references/rag-patterns.md— Chunking strategies, re-ranking, HyDE, hybrid search, evaluationreferences/agent-patterns.md— Tool calling, multi-step loops, memory, error handling