What Is a Baseline Distribution?

What Is a Baseline Distribution?

A baseline distribution is the reference shape of data, traffic, labels, and evaluator scores that a team treats as normal for an LLM or agent system. It is a data-reliability concept used in eval pipelines, production traces, and drift monitoring to compare current behavior against an approved reference. In FutureAGI, a baseline distribution usually lives in a sdk:Dataset so engineers can compare cohorts, prompts, models, tool paths, and scores before shipping a change.

Why It Matters in Production LLM and Agent Systems

The failure mode is quiet drift. A support agent can keep its global pass rate steady while the traffic mix shifts toward refund disputes, a new locale, or tool-heavy workflows. A RAG system can pass yesterday’s eval set while today’s user queries pull from a different product area. Without a baseline distribution, these changes look like ordinary variance until the team sees more hallucinations, failed handoffs, or high-cost traces.

Developers feel it when a prompt release fails only for one cohort. SREs see p99 latency and token-cost-per-trace rise, but cannot tell whether the model slowed down or the request mix changed. Product teams see conversion fall after a launch and lack the distribution context to separate model quality from audience change. Compliance reviewers need evidence that protected or regulated cohorts stayed inside expected behavior, not only that the average score passed.

The logs usually show symptoms before the incident report: a spike in out-of-distribution intents, lower ContextRelevance for one content source, more agent.trajectory.step retries, or evaluator failures concentrated in a single prompt version. Baselines matter more in 2026 multi-step pipelines because agent behavior is distribution-sensitive. A single-turn answer may still look acceptable while the planner chooses different tools, consumes more context, or routes users through a less tested path.

How FutureAGI Handles Baseline Distributions

FutureAGI’s approach is to treat the baseline distribution as an eval asset, not a spreadsheet someone remembers to check. The anchor surface is sdk:Dataset, implemented through fi.datasets.Dataset in the SDK inventory. A team can create a Dataset from approved production traces, annotated eval rows, imported files, or generated scenario rows, then keep columns such as input, expected_output, cohort, intent, locale, prompt_version, model, trace_id, and tool_path.

The workflow is concrete. Suppose a fintech team has a chargeback-support agent. They snapshot 5,000 approved 2026 traces into a baseline Dataset, grouped by dispute reason, amount band, locale, and required tool. They attach Dataset.add_evaluation runs that score each row with Groundedness for context support, ContextRelevance for retrieved evidence quality, and ToolSelectionAccuracy for agent tool choice. The team then compares a candidate prompt and model route against the same baseline cohorts.

The exact signals are distribution distance plus eval movement: population-stability-index by cohort, Jensen-Shannon divergence for intent mix, and eval-fail-rate-by-cohort for each evaluator. If “high amount, missing receipt” traffic moves outside the baseline and ToolSelectionAccuracy drops, the engineer does not ship blindly. They add that slice to a regression eval, alert on the cohort, or require human review for the affected route. Unlike a Ragas-only faithfulness check, which can miss traffic-shape changes, this keeps the data distribution and the evaluator scores tied together.

How to Measure or Detect It

Measure a baseline distribution by comparing the approved reference Dataset with a current sample from traces, eval runs, or release candidates.

• Distribution distance: use population-stability-index for binned cohorts and Jensen-Shannon divergence for categorical mixes such as intent, locale, model, or tool path.
• Evaluator deltas: Groundedness checks whether a response is supported by context; ContextRelevance checks whether retrieved context fits the task.
• Trace fields: compare llm.token_count.prompt, agent.trajectory.step, model name, route, retry count, and tool path between baseline and current traces.
• Dashboard signals: watch eval-fail-rate-by-cohort, token-cost-per-trace, latency p99 by route, and out-of-distribution row count.
• User proxy: confirm drift with thumbs-down rate, escalation-rate, refund rate, or manual-review rate by the same cohort keys.

Minimal Python sketch:

from fi.evals import Groundedness, ContextRelevance

evaluators = [Groundedness(), ContextRelevance()]
baseline_scores = run_dataset_eval("support-baseline-2026", evaluators)
current_scores = run_dataset_eval("support-current", evaluators)
print(compare_by_cohort(baseline_scores, current_scores))

Common Mistakes

Most baseline mistakes come from making the reference too broad or too stale:

• Using all historical traffic as normal. Incidents, migrations, and early experiments pollute the baseline unless they are filtered or labeled.
• Comparing averages only. A flat overall score can hide drift in high-risk cohorts, low-volume locales, or tool-heavy agent paths.
• Changing evals and baseline together. If the evaluator changes, freeze the old baseline scores before interpreting new deltas.
• Ignoring prompt and model version. A data shift can be a rollout artifact; include prompt_version, model, and route in the baseline columns.
• Letting the baseline age silently. Refresh on planned release windows, not after a production complaint proves the reference is stale.