What Is a Current Distribution?

What Is a Current Distribution?

A current distribution is the live statistical shape of the data an AI system is receiving now: prompts, retrieved context, labels, tool outputs, or trace fields. It is a data-family reliability concept that appears in production traces, evaluation datasets, drift dashboards, and regression pipelines. Engineers compare the current distribution with a baseline or reference distribution to detect cohort shifts before quality scores mislead them. In FutureAGI, it is managed through fi.datasets.Dataset cohorts and evaluator trends.

Why Current Distributions Matter in Production LLM and Agent Systems

Ignoring the current distribution lets stale test evidence pass as production truth. A RAG assistant can look healthy on last month’s billing questions while live users now ask about a new cancellation policy. A support agent can pass an English-only golden dataset while production traffic shifts toward multilingual refund requests. The failure mode is usually not a loud crash; it is a false eval pass, followed by silent hallucinations downstream of a retriever, wrong tool routing, or rising refusals in a narrow cohort.

Developers feel it as hard-to-reproduce failures: the trace that failed does not look like any row in the eval set. SREs see p95 latency climb because the agent takes extra retrieval or tool steps. Product teams see thumbs-down spikes in one segment while the global pass rate stays flat. Compliance teams lose evidence that the latest policy, locale, or customer tier was actually tested.

The risk is sharper in 2026-era multi-step systems. One shifted input distribution can change the planner’s path, expand prompt context, alter tool outputs, and produce a confident final answer that no offline row covered. Symptoms include new top intents, longer prompts, unfamiliar entities, changing llm.token_count.prompt buckets, lower ContextRelevance, and rising eval-fail-rate-by-cohort after a traffic or document refresh.

How FutureAGI Handles Current Distributions

FutureAGI anchors current-distribution work to sdk:Dataset, exposed as fi.datasets.Dataset. A practical workflow starts by storing the reference window and the current window as comparable dataset cohorts. Each row can carry input, retrieved_context, expected_response, source_trace_id, cohort, distribution_window, dataset_version, and product metadata such as locale, plan, or policy_version.

The engineer then compares the current cohort with the reference cohort using distribution metrics outside the model call: population stability index for bucketed fields, Jensen-Shannon divergence for probability distributions, Wasserstein distance for ordered numeric signals, and embedding-distance movement for prompt clusters. FutureAGI’s approach is to bind those measurements to rows that can be evaluated, reviewed, and promoted into regression suites, not to leave them as detached analytics.

For example, a LangChain RAG system instrumented with traceAI’s langchain integration may show a new cluster of enterprise renewal questions. The team imports sampled traces into fi.datasets.Dataset, tags them as distribution_window="current", and reruns ContextRelevance and Groundedness. If the current cohort fails because retrieved context is stale, the next action is a corpus refresh and a regression eval. If the context is good but outputs still fail, the team updates the prompt or adds a model fallback in Agent Command Center. Unlike a Great Expectations table check, this connects live-data movement to model, retrieval, and agent behavior.

How to Measure or Detect a Current Distribution

Measure the current distribution against a named reference window, then inspect whether quality moved with it:

• Categorical shift: compare intent, locale, product tier, tool path, and policy-version shares against the reference distribution.
• Text shift: cluster prompt embeddings and retrieved chunks; watch for new clusters, entity changes, and long-tail growth.
• Numeric shift: monitor token counts, context age, tool latency, retry count, and row-level cost with PSI or Wasserstein distance.
• Evaluator movement: track ContextRelevance and Groundedness by cohort so distribution shift is tied to answer quality.
• Dashboard signal: alert on eval-fail-rate-by-cohort, retrieval zero-result rate, thumbs-down rate, escalation rate, and reviewer-disagreement rate.

from fi.evals import ContextRelevance

scorer = ContextRelevance()
result = scorer.evaluate(
    input=row["input"],
    context=row["retrieved_context"],
)
print(result.score, result.reason)

A single shifted field is not automatically a release blocker. Treat it as a triage signal: if the new current distribution also lowers evaluator scores or user-feedback proxies, promote representative rows into the dataset and rerun regression evals.

Common Mistakes

• Comparing live traffic with an unnamed baseline. Without a dated reference window, nobody can reproduce or explain the shift.
• Tracking only prompt text. Current distributions also include retrieved context, tool outputs, labels, trace fields, and user cohorts.
• Using global averages. A stable overall distribution can hide a severe shift for one locale, tenant, policy, or route.
• Calling every distribution change a bug. A launch can create healthy new traffic; quality movement decides whether it is risky.
• Updating eval rows after detection without versioning. Silent dataset edits erase the evidence needed for release comparisons.