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Seer turns unlabeled retrieval traffic into actionable quality signals. This page defines each metric, how we compute it, and how to read it in the app.
New here? First see Context & Event schema to understand the input shapes Seer consumes.

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Overview

For each logged retrieval event, Seer:
  1. Uses an evaluator model to enumerate the minimal disjoint requirements needed to answer the query (we call this number K).
  2. Judges which requirements are supported by the retrieved context you sent.
  3. Produces Recall, a Precision proxy, and derived scores (F1, nDCG).
These metrics are designed to be:
  • Model-agnostic (work with any retriever/search stack).
  • Label-free (computed without ground-truth annotations).
  • Ops-friendly (good default thresholds, alertable, SLO-able).

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Evaluator-defined Recall

What it measures: “Did my context include everything needed to answer?” Definition
  • The evaluator lists requirements: r1..rK.
  • For each requirement ri, it marks it present if at least one passage in your context supports it.
  • Recall is:
recall = (# of requirements marked present) / K
Interpretation
  • 1.0 ⇒ every requirement was supported by your retrieved context.
  • < 1.0 ⇒ at least one needed requirement was missing (Seer flags these).
Notes
  • K varies by query. Single-hop questions tend to have smaller K; multi-hop and compositional queries have larger K.
  • Your context can be either string[] or { text, ... }[]. If object shape is used, evaluator relies on the text field.

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Precision (proxy for context bloat)

What it measures: “How much of my context actually helped?” Because there’s no gold set of “all relevant documents,” we use a proxy precision: 
precision = (# of unique documents the evaluator cites as supporting any requirement) / (total # of documents in context)
  • If your context is large but the evaluator only cites a couple items, precision will be low. This signals context bloat.
  • If you provide ranks or scores on each context item (e.g., rank, score), they help downstream metrics like nDCG.
We will introduce citation precision (based on downstream answer citation) later; the above is retrieval-only.

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F1 (derived)

We derive F1 from Recall and the Precision proxy: 
f1 = 2 * (precision * recall) / (precision + recall)
  • Behaves as expected: penalizes if either recall or precision is poor.

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nDCG (optional ranks/scores)

If your context items include rank (1 = top) or a score, Seer computes an nDCG-style score using the evaluator’s “supporting/not” signal as graded relevance. High-level:
  • Relevance: 1 for “supporting”, 0 for “not supporting” (or a small fractional value if we add soft support later).
  • DCG is computed over your ranked list; IDCG is the ideal ordering (all supporting first).
  • nDCG = DCG / IDCG in [0,1].
If you don’t provide ranks/scores, we still compute recall/precision/F1. nDCG appears when ranking data is present.

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Worked example

Input (your log) 
{
  "task": "Who directed Inception and what is their nationality?",
  "context": [
    {"id": "p1", "text": "Christopher Nolan directed Inception.", "score": 0.95},
    {"id": "p2", "text": "Nolan is British-American.", "score": 0.89},
    {"id": "p3", "text": "Inception released in 2010.", "score": 0.72}
  ],
  "metadata": {
    "env": "prod",
    "feature_flag": "retrieval-v2"
  }
}
Evaluator (conceptual output)
  • Requirements (K=2):
    • f1) The director of Inception
    • f2) The nationality of that director
  • Present:
  • f1 supported by {p1}
  • f2 supported by {p2}
  • Missing: none
Metrics
  • Recall = 2/2 = 1.0
  • Precision proxy = supporting documents {p1, p2} / total documents 3 = 0.67
  • F1 = 2*(1.0*0.67)/(1.0+0.67) ≈ 0.80
  • nDCG (using scores for ordering): supporting documents at positions 1 and 2 ⇒ nDCG ≈ 1.0 (near-ideal order)
  • Recall: alert at < 1.0 for high-priority surfaces; or at < 0.8–0.9 for lower priority.
  • Precision (proxy): watch long tails where precision < 0.3–0.4 ⇒ context bloat.
  • F1: use as a simple roll-up; alert if it drops by Δ 0.15–0.25 release-over-release.
  • nDCG: if you use ranking, alert on material drops (e.g., Δ 0.1).
You can scope alerts by env, feature_flag, or any metadata field (e.g., tenant, product area). See Alerting for setup instructions.

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FAQ

Do I need labeled data? No. The evaluator determines requirements (K) and support directly from your retrieval outputs. Does recall depend on my final LLM’s answer? No. These are retrieval-stage metrics. (Answer-stage citation metrics are planned separately.) How do I improve precision without hurting recall? Tune rankers/rerankers, filter boilerplate, and trim redundant passages. nDCG helps validate ordering changes. What if my context is string[] and not objects? Totally fine. If you later add {id, rank, score}, you’ll unlock deeper analytics like nDCG and per-passage views.

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See Also