How is an evaluation window different from an evaluation metric?

An evaluation metric is the scoring function, such as Groundedness or ContextRelevance. An evaluation window is the sample boundary that decides which rows or traces those metrics run on.

How do you measure an evaluation window?

Run FutureAGI evaluators such as Groundedness, ContextRelevance, or HallucinationScore over a fixed `fi.datasets.Dataset` slice. Compare score distributions, fail rate, sample count, and user-feedback proxies by `window_start` and `window_end`.

What Is an Evaluation Window? Definition & FutureAGI Guide (2026)

What Is an Evaluation Window?

An evaluation window is the bounded slice of time, traffic, cohort, or dataset rows used to calculate LLM evaluation results. It is an eval-pipeline control: the window decides which production traces, prompts, model versions, and user segments are included before metrics such as Groundedness or HallucinationScore are averaged. In FutureAGI, engineers attach evals to a fi.datasets.Dataset and compare windows so release scores, drift alerts, and regression gates are based on matching samples.

Why Evaluation Windows Matter in Production

Bad windows create bad release decisions. If a customer-support agent is evaluated on traffic from the last day, but the previous release used a curated holdout set, a higher score may only mean the second window was easier. That leads to false passes, missed hallucinations, and eval drift that looks like model improvement. A retriever fix can also look worse if the window suddenly includes long-tail accounts, multilingual queries, or a new policy topic.

The pain lands on several teams. Developers chase regressions that are really sampling differences. SREs see alert noise because thresholds were tuned on a quiet weekday window and then applied to peak usage. Compliance teams cannot prove whether a risky answer pattern improved because the before and after samples do not match. Product teams get confused when thumbs-down rate rises while offline eval scores stay flat.

Common symptoms are uneven row counts, sudden changes in language mix, score swings without code changes, and high variance in small windows. For 2026-era agentic systems, the risk is larger because one user task can span retrieval, planning, tool calls, and final response generation. Unlike a static Ragas test set, an evaluation window must preserve the production boundary that created those steps.

How FutureAGI Handles Evaluation Windows

FutureAGI’s approach is to make the window a first-class data boundary before any evaluator score is trusted. The anchor surface is fi.datasets.Dataset: teams create or import rows, add columns such as window_start, window_end, release_version, traffic_cohort, and trace_id, then attach evaluations with Dataset.add_evaluation. That keeps the sample definition next to the prompts, outputs, contexts, and eval stats.

A real workflow: an engineer samples 2,000 support-agent traces from May 1 to May 7, 2026, using the langchain traceAI integration as the source. They load those rows into a Dataset named support-agent-release-2026-05-07, attach Groundedness for citation support, ContextRelevance for retrieved-doc fit, and HallucinationScore for unsupported claims. The release gate compares this window only against the prior May 1 to May 7 baseline, not against a handpicked golden set from April.

The next action is concrete. If Groundedness drops below 0.82 for enterprise accounts, the engineer opens the failing rows, checks retrieved chunks, and rolls back the retriever change or raises a regression eval. If only p99 latency or llm.token_count.prompt changed while quality stayed flat, the fix is routing or context trimming, not a model rollback. The window prevents teams from mixing root causes.

How to Measure or Detect a Bad Evaluation Window

Track the window itself before interpreting evaluator results:

Sample count and cohort mix: compare row counts, language, account tier, route, and model version between windows.
Evaluator distribution: Groundedness returns a context-support score; compare mean, p10, and fail rate, not only the average.
Trace fields: check trace_id, timestamp, llm.token_count.prompt, and p99 latency to confirm the window maps to the intended production slice.
Dashboard signal: alert on eval-fail-rate-by-cohort when the same metric threshold fails only in one traffic segment.
User-feedback proxy: compare thumbs-down rate, escalation rate, and manual review rejects for the same window.

Minimal evaluator check:

from fi.evals import Groundedness

groundedness = Groundedness()
result = groundedness.evaluate(
    input="How do I reset SSO?",
    output="Ask an admin to rotate the SSO certificate.",
    context=["SSO reset requires an admin certificate rotation."],
)
print(result.score)

Run the same evaluator over every row in the selected Dataset window, then compare the distribution with the prior matching window.

Common Mistakes

The mistakes usually come from treating a window as an afterthought instead of part of the eval design.

Comparing fixed holdout data to live traffic. The live window has harder cases, user noise, and fresh product topics.
Using windows with too few rows. A 40-trace window can swing wildly; set minimum sample counts before alerting.
Changing the cohort mid-release. Mixing free-tier and enterprise traffic hides regressions in the group that matters most.
Averaging across model versions. If two models served one window, split scores before deciding which release failed.
Ignoring failed or timed-out traces. Dropped rows make agents look better by removing the hardest production cases.