Engineering deep-dives, product updates, and lessons from building simulation infrastructure for AI systems.
Claiming ground-truth dynamic accuracy for RAG pipelines without live testing is intellectually dishonest. Here is why we ripped out our accuracy scoring and replaced it with Configuration Intelligence.
Most RAG pipelines make 2-3x more LLM calls than necessary. Across the 40+ teams in our private beta, we found that 40% of token spend goes to calls that could be eliminated with proper routing and caching.
A step-by-step walkthrough of using PRISM's latency profiler to identify and fix a re-ranker bottleneck that was adding 11 seconds to every request in a production document QA system.
How we built a deterministic cost calculator and a 10,000-iteration Monte Carlo latency profiler without needing actual API calls. The architecture decisions, trade-offs, and statistical models.
Datadog, Grafana, and New Relic were built for deterministic request-response services. AI pipelines are multi-stage, non-deterministic, and token-metered. Here's why they need purpose-built tooling.
Today we're opening PRISM to 40 engineering teams. Build your pipeline visually, run statistical traffic distributions, and find cost and latency bottlenecks before they reach your users.
Bi-weekly engineering reports on AI pipeline architectures, cost-reduction strategies, and simulation benchmarks.