OpenAI DevDay 2024 | Balancing accuracy, latency, and cost at scale

Scaling an LLM-powered app from thousands to millions of users forces hard tradeoffs between accuracy, latency, and cost—and the most reliable path is to treat optimization as a measurable loop: set an accuracy target with evals, then “hill climb” toward it using prompt engineering, RAG, and fine-tuning, and only after that optimize speed and spend. The practical payoff is straightforward: once accuracy is locked in, reducing latency and per-request cost increases the number of inferences possible within the same budget, which can translate into better overall product performance.

OpenAI frames the process as a two-stage pipeline. First, accuracy: builders should define an accuracy target in business terms (for example, “90% of customer service tickets are routed correctly on the first attempt”), then build baseline evaluations to measure end-to-end behavior in production-like conditions. Evals come in two main forms—component evals (unit-test style checks for individual steps) and end-to-end evals (black-box tests that run the full workflow from input to final output). A key best practice for scaling complex systems—especially customer service networks with multiple intents, assistants, and tools—is to mine historic conversations, have LLMs simulate customers, and run them through the routing/instruction network to detect regressions and “divergence” when changes elsewhere break earlier assumptions. In one example, a customer service setup expanded from 50 routines to over 400, with more than 1,000 evals rerun after material changes.

Second, once the accuracy target is agreed upon, optimization focuses on reaching it at the lowest cost. OpenAI describes a “four-box” approach: start with prompt engineering (explicit instructions and iterative eval-driven debugging), then move to retrieval augmented generation (RAG) when missing context is the failure mode, and fine-tuning when the model struggles with instruction-following consistency or needs more examples. Recent shifts include using long context to scale prompt engineering (without fully replacing RAG), and automating prompt optimization via “meta prompting,” where a model iterates prompts based on eval results—accelerated by models like o1 for tasks such as generating and optimizing customer service routines.

A concrete accuracy case study starts from a weak baseline (45% accuracy using cosine similarity retrieval) in a regulated domain. The team set a tolerance that prioritized false negatives over false positives to reduce harmful hallucinations, then pushed toward a 95% target. Improvements came from chunking and embedding grid searches, adding a classification step to choose keyword vs semantic search, introducing a domain-specific reranker, and handling analytical questions with SQL generation plus query expansion.

After accuracy is achieved, latency and cost become the next levers. Latency is decomposed into network latency, time to first token (prompt processing), and output latency (time between tokens). Output latency dominates for most workloads, but exceptions exist (e.g., long-document classifiers). OpenAI highlights practical tactics: reduce prompt length where possible, choose smaller models when feasible (e.g., o1 mini vs larger options), and cut output tokens by prompting for concise answers. Network latency is also being reduced through regional routing so requests are processed closer to where they originate.

Cost optimization overlaps with latency work: fewer tokens generally means both faster responses and lower spend. OpenAI emphasizes operational controls (per-project usage limits and alerts), then introduces two major efficiency tools. Prompt caching launches with a prefix-match requirement—static instructions and examples should be placed at the start of prompts so repeated requests reuse cached activations (with savings of 50% on cached tokens). BatchAPI offers another 50% discount on both prompt and output tokens by running large asynchronous workloads within up to 24 hours, without consuming regular rate limits. Echo AI’s customer-call categorization workflow illustrates the trade: near-real-time processing via batch jobs can cut costs dramatically while still supporting scalable operations.

Overall, the central message is that there is no single playbook. The art is selecting the right tradeoffs among accuracy, latency, and cost—using eval-driven development to make those tradeoffs measurable and safe.