The most effective AI product managers aren't those who can write machine learning code—they're the ones who understand enough about how AI systems work to make informed product decisions, communicate credibly with engineers, and identify what's actually possible versus what's science fiction. This chapter will transform you from someone who nods along in technical discussions to someone who asks the right questions and catches flawed assumptions before they become expensive mistakes.
123456789101112# Simple token cost calculator for product planning def calculate_monthly_cost( daily_requests: int, avg_input_tokens: int, avg_output_tokens: int, input_cost_per_million: float, # e.g., $10 for GPT-4 Turbo output_cost_per_million: float # e.g., $30 for GPT-4 Turbo ) -> dict: monthly_requests = daily_requests * 30 input_cost = (monthly_requests * avg_input_tokens / 1_000_000) * input_cost_per_million
123456789101112from openai import OpenAI from pinecone import Pinecone client = OpenAI() pc = Pinecone(api_key="your-key") index = pc.Index("knowledge-base") def answer_question(user_query: str, top_k: int = 5) -> dict: # Step 1: Embed the query query_embedding = client.embeddings.create( input=user_query, model="text-embedding-3-small"
123456789101112from openai import OpenAI import numpy as np from typing import List, Dict client = OpenAI() # Step 1: Create embeddings for your knowledge base def create_embedding(text: str) -> List[float]: response = client.embeddings.create( model="text-embedding-3-small", input=text )
123456789101112import OpenAI from 'openai'; const openai = new OpenAI(); async function streamResponse(userMessage: string): Promise<void> { const stream = await openai.chat.completions.create({ model: 'gpt-4-turbo-preview', messages: [{ role: 'user', content: userMessage }], stream: true, }); // Track time to first token for latency monitoring