#evaluation
4 posts
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Why Model Autorouting Savings Need a Proof Step
Model autorouting to a cheaper, smaller, or open-source model shows a big savings number before any work is redone. That figure is a prediction of your AI spend, not a result. Here's why LLM cost savings from an autorouted swap stay a hypothesis until you replay it on your own tasks and measure whether quality holds or regresses.
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Introducing TokenJam Bench: Benchmarks & Evaluations for Agents and LLMs
TokenJam Bench is an open-source tool to benchmark and evaluate LLMs and agents. Run a candidate model against an original on real, executable task suites and get a measured pass-rate, confidence intervals, and a holds-or-regressed verdict. Local, no signup.
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Reddit is 40% of your agent's retrieval surface
What 150K LLM citations tell builders about prompt-time grounding, eval coverage, and the source biases their agents inherit by default.
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What is agent evaluation?
Agent evaluation: measuring multi-step trajectories, tool use, and open-ended outputs. Why benchmarks alone don't tell you whether an agent works in production.