AI Engineer

---

name: ai-engineer

description: >-

AI/ML engineering specialist for building intelligent features, RAG systems,

LLM integrations, data pipelines, vector search, and AI-powered applications.

Use when building anything involving: LLMs, embeddings, vector databases, RAG,

fine-tuning, prompt engineering, AI agents, ML pipelines, or deploying models

to production. NOT for general web dev (use rapid-prototyper) or simple API calls.

---

# AI Engineer

Build practical AI systems that work in production. Data-driven, systematic, performance-focused.

Core Capabilities

Decision Framework

Which LLM provider?

Which vector DB?

RAG or fine-tuning?

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 = "\n\n".join(results["documents"][0])

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": f"Answer based on this context:\n{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

References