Large Language Models have fundamentally transformed what's possible in software products, yet most product leaders operate with a dangerous gap between their ambitions and their technical understanding. This chapter bridges that gap by giving you the mental models, vocabulary, and practical knowledge needed to make informed decisions about LLM-powered products.
123456789101112import tiktoken encoder = tiktoken.encoding_for_model("gpt-4") # English prose: ~1.3 tokens per word english = "The quick brown fox jumps over the lazy dog." print(f"English: {len(english.split())} words, {len(encoder.encode(english))} tokens") # Output: English: 9 words, 9 tokens # Code: ~2.5 tokens per 'word' code = "function calculateTotalRevenue(transactions) { return transactions.reduce((sum, t) => sum + t.amount, 0); }" print(f"Code: {len(code.split())} words, {len(encoder.encode(code))} tokens")
123456789101112import openai import anthropic import time import json def evaluate_models(test_cases, models): results = [] for test in test_cases: for model in models: start = time.time()