How to Calculate RAG Knowledge Half-Life
Retrieval-augmented generation (RAG) teams increasingly manage knowledge bases whose usefulness decays as policies, product content, and regulatory language change. Without a quantitative understanding of that decay curve, refresh sprints either lag behind reality or consume scarce subject-matter expertise prematurely. The concept of a knowledge half-life—how long it takes for half of validated answers to go stale—gives operations and compliance stakeholders a common language for prioritising updates.
This walkthrough shows how to estimate the half-life directly from freshness audits. We link evaluation telemetry to an exponential decay model, derive the governing equations, document each variable and unit, and finish with a step-by-step workflow you can operationalise alongside observability tools. The method complements quality metrics such as retrieval recall at k and latency budgeting guidance in the GenAI P95 latency budget article, giving you a cohesive operating picture.
Define the knowledge boundary and sampling approach
Begin by deciding which slices of the knowledge base you will monitor. Most teams group content by policy domain, geography, or product line because decay rates vary widely. Specify the retrieval index, metadata filters, and answer formats you include so the resulting half-life speaks to a clearly scoped corpus. Document the observation window—often 14, 30, or 60 days—between the baseline snapshot and the audit in which stale responses were discovered.
Sampling discipline matters. Stratified samples aligned with traffic weight provide more reliable decay measurements than pure random draws. Where feasible, let the evaluation platform capture how much real traffic the reviewed queries represent; you will use that coverage metric later to correct for partial visibility.
Variables, notation, and units
Use the following definitions consistently across audits:
- t – Observation window length (days) between baseline and audit.
- Na – Number of answers evaluated for freshness (dimensionless count).
- Ns – Count of evaluated answers marked stale, outdated, or incomplete (dimensionless count).
- c – Evaluation coverage (fraction of total traffic or answer volume represented, dimensionless).
- f – Adjusted stale fraction after correcting for coverage (dimensionless).
- λ – Decay constant (per day) describing the proportional rate at which answers become stale.
- T½ – Knowledge half-life (days) for the corpus.
- Ftarget – Minimum acceptable freshness fraction (dimensionless), typically 0.8–0.95.
- τ – Refresh cadence (days) required to maintain the target freshness.
Capture metadata such as the evaluation rubric, severity definitions, and whether partial credit answers are considered stale. The calculator treats staleness as binary, so align your rubric accordingly before aggregating results.
Derive the exponential decay relationships
The freshness of a knowledge base often follows an exponential decay because the probability of an answer becoming outdated in a small interval is roughly proportional to the amount of fresh content remaining. Under that assumption, the fraction of fresh answers after time t is F(t) = e−λt. The freshness fraction observed during the audit equals the proportion of evaluated answers that remained current after adjusting for coverage.
c = max(0.01, min(1, coveragereported))
f = min(0.999, (Ns / Na) / c)
F(t) = 1 − f
λ = −ln(F(t)) / t
T½ = ln 2 / λ
τ = −ln(Ftarget) / λ
The coverage adjustment inflates the stale fraction when evaluations touch only a subset of traffic. If you reviewed 60% of answers, a 15% stale rate in that sample implies a 25% effective stale rate across the whole corpus. The cap at 0.999 prevents mathematical singularities when everything appears stale.
Step-by-step calculation workflow
1. Collect evaluation telemetry
Export evaluation logs with timestamps, question identifiers, reviewer decisions, and severity tags. Confirm that the sample window aligns with the baseline snapshot used to define freshness. If you operate multiple indices, segment the export by index to avoid blending heterogeneous decay rates.
2. Normalize counts and coverage
Aggregate reviewed answers (Na) and stale findings (Ns) across the observation window. Convert reported coverage into a fraction c between 0 and 1. When coverage is unknown, assume 100% but document that assumption and tighten sampling in the next cycle.
3. Compute decay metrics
Apply the equations above to derive the decay constant and half-life. Use high-precision arithmetic to avoid rounding bias, then format results to two decimal places for reporting. If the derived half-life is extremely long (hundreds of days), verify that the stale rate is non-zero and that reviewers are not overlooking subtle changes.
4. Translate half-life into refresh cadence
Decide on a target freshness fraction Ftarget. Highly regulated customer support assistants might require 95% of answers to remain current, whereas internal productivity copilots can tolerate 75–80%. Plug that threshold into the formula for τ to obtain the number of days between refresh sprints.
5. Integrate with operational planning
Feed the half-life and cadence into sprint planning and workforce allocation. Combine the results with evaluation budgeting tools such as the GenAI QA budget planner to size reviewer hours and contract spend. Align release calendars so knowledge updates and model deployments remain synchronised.
Validation and quality assurance
Cross-check the derived half-life against historical incidents. If your assistant required major content updates every six weeks previously, but the new calculation suggests six months, inspect sampling bias, reviewer calibration, or abrupt policy freezes that could have lowered decay temporarily. Validate coverage metrics by comparing sampled traffic to analytics logs or telemetry from your RAG orchestrator.
Run sensitivity tests: adjust the stale fraction by ±5 percentage points and recompute the half-life. Large swings indicate a need for larger sample sizes. Keep a version-controlled notebook that stores raw counts, computed λ, half-life, and recommended cadence for each cycle so auditors and stakeholders can retrace the reasoning.
Limits and interpretation
The exponential model assumes independence between answers and a constant decay rate. In practice, release trains, major product launches, or regulatory events can produce step changes. Treat the half-life as an average behaviour between such shocks and supplement it with qualitative risk registers. Monitor per-domain decay, because legal policies might decay more slowly than promotional content.
Remember that freshness is only one dimension of RAG quality. Pair the half-life with retrieval recall, grounding compliance, and latency metrics to avoid over-optimising for content updates at the expense of user experience.
Embed: RAG knowledge half-life calculator
Enter the observation window, evaluated sample counts, stale findings, and optional coverage or freshness thresholds to compute decay rate, half-life, and refresh cadence for your RAG corpus.