Cost, Latency, and Throughput Optimization

Learn how to balance speed, cost, and quality when usage grows from prototype to production.

Explanation

Model choice, context length, output size, and retry behavior all affect cost and latency.

Caching, batching, and prompt compression can reduce waste.

Optimization should preserve user value instead of only cutting expense.

Why this topic matters in practice

In generative AI products, the model is only one part of the system. The surrounding workflow determines whether the output is useful, safe, and maintainable. This lesson matters because it helps you connect the idea to tasks such as tutoring, search, copilots, business assistants, and production automation.

Examples

A simple response cache

The code below is intentionally concise so the underlying pattern stays clear. It focuses on the application logic you can reuse, even if you later switch model providers or deployment environments.

cache = {}

def get_answer(question):
    if question in cache:
        return "[cached] " + cache[question]

    answer = f"Generated answer for: {question}"
    cache[question] = answer
    return answer

print(get_answer("What is RAG?"))
print(get_answer("What is RAG?"))

How the coding section works

• Caching is one of the fastest ways to reduce repeated model calls.
• In production, cache invalidation and freshness rules matter.
• Optimization is most effective when you first understand usage patterns.

Implementation advice

When turning this lesson into a real feature, think beyond the code snippet itself. Decide what inputs should be allowed, how you will validate outputs, how you will recover from errors, and how you will measure whether the feature is actually helping users. Those surrounding choices often determine whether an AI feature feels polished or unreliable.