AI Engineering

Build production LLM applications and AI systems.

When to Use

• Integrating LLM APIs

• Building RAG systems

• Creating AI agents

• Vector database setup

• Token optimization

LLM Integration

API Setup

from anthropic import Anthropic

client = Anthropic()

def chat(messages: list[dict], system: str = None) -> str: response = client.messages.create( model="claude-sonnet-4-20250514", max_tokens=1024, system=system or "You are a helpful assistant.", messages=messages ) return response.content[0].text

With retry and error handling

from tenacity import retry, stop_after_attempt, wait_exponential

@retry(stop=stop_after_attempt(3), wait=wait_exponential(min=1, max=10)) def safe_chat(messages, system=None): try: return chat(messages, system) except Exception as e: logger.error(f"LLM call failed: {e}") raise

Structured Output

import json

def extract_structured(text: str, schema: dict) -> dict: prompt = f"""Extract information from the text according to this schema: {json.dumps(schema, indent=2)}

Text: {text}

Return valid JSON only."""

response = chat([{"role": "user", "content": prompt}])
return json.loads(response)

RAG System

Document Processing

from langchain.text_splitter import RecursiveCharacterTextSplitter

def chunk_documents(docs: list[str], chunk_size=1000, overlap=200): splitter = RecursiveCharacterTextSplitter( chunk_size=chunk_size, chunk_overlap=overlap, separators=["\n\n", "\n", ". ", " "] ) return splitter.split_documents(docs)

Vector Store

from qdrant_client import QdrantClient from qdrant_client.models import Distance, VectorParams

client = QdrantClient(":memory:") # or url="http://localhost:6333"

Create collection

client.create_collection( collection_name="docs", vectors_config=VectorParams(size=1536, distance=Distance.COSINE) )

Upsert vectors

client.upsert( collection_name="docs", points=[ {"id": i, "vector": embed(chunk), "payload": {"text": chunk}} for i, chunk in enumerate(chunks) ] )

Search

results = client.search( collection_name="docs", query_vector=embed(query), limit=5 )

RAG Query

def rag_query(question: str, top_k=5) -> str: # Retrieve relevant chunks results = client.search( collection_name="docs", query_vector=embed(question), limit=top_k )

context = "\n\n".join([r.payload["text"] for r in results])

prompt = f"""Answer based on the context below.

Context: {context}

Question: {question}

Answer:"""

return chat([{"role": "user", "content": prompt}])

Cost Optimization

• Cache frequent queries

• Use smaller models for simple tasks

• Batch requests when possible

• Track token usage per feature

• Set max_tokens appropriately

Examples

Input: "Add AI chat to this app" Action: Set up LLM client, create chat endpoint, add error handling

Input: "Build RAG for documentation" Action: Chunk docs, create embeddings, set up vector store, implement search