What you will learn
Latency budgets, Caching, Batching, Quantization, Load testing.
Engineering the capability
Optimize the whole request, not just tokens per second. Measure queue wait, retrieval, first output, decoding, and validation separately. Streaming can improve perceived responsiveness while leaving total completion time unchanged. Batch processing may improve throughput but increase waiting time for an individual request.
Cache only when the key includes all inputs that affect the permitted answer: model and prompt versions, relevant data version, and authorization scope. A semantic cache adds another error mode because similar questions may need different answers. Validate it before treating cache hits as free quality.
Quantization reduces numerical precision and can reduce memory requirements, but its quality and speed effects depend on hardware and workload. Compare on the actual task. Track cost per accepted result, including retries and failed attempts; a cheaper model can be more expensive if it requires many repairs.
Worked case
Model A costs 1 unit per attempt and succeeds 50% of the time; model B costs 1.5 and succeeds 90%. Ignoring retries and assuming one attempt, cost per success is 2 for A and about 1.67 for B. Real retry and fallback behavior changes the calculation, so log outcomes rather than comparing only listed token prices.
Put it into practice
Continue with the next lab: build a latency and cost budget. Build the artifact, record the failure cases, and explain the tradeoff before moving on.