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Coverage: The Third of Your Index That Nothing Tests

Name: System Design Masterclass
Availability: InStock

Thirty Cases Is Not a Coverage Number

Every eval set is described the same way. How many cases. Thirty, three hundred, three thousand.

An engineer with a torch in front of a tall shelf of identical file boxes, only a few of them lit while the rest stay in shadow. Beside them: thirty cases, fifteen documents, ten of them tested. The case count says how much work went into the set and nothing about which parts of the index the set can see.

That number says how much work went in. It says nothing about what the set can see.

A ledger split in two. On the left, under what gets reported, the number thirty set very large, labelled cases in the set, the number everyone quotes, describing the effort rather than the reach. On the right, under what it covers, ten out of fifteen set very large, labelled documents ever tested, with the note that five documents are never the right answer to any case in the set.

Join the set against the thing it is testing and a different question appears. Of the documents your retriever can actually return, how many has any case ever pointed at?

On the thirty traces from the last two lessons, against the fifteen documents they search, the answer is ten.

Five Documents, Never the Right Answer to Anything

A grid of fifteen cards, one per document in the index. Ten are filled and read three cases each: D01, D02, D03, D05, D06, D07, D08, D09, D10 and D12. Five are dashed outlines reading none: D04, D11, D13, D14 and D15. The line beneath says nothing in the set asks a question they are the answer to.

Five of the fifteen are never the gold answer to any case: D04, D11, D13, D14 and D15.

That is a third of the index. If one of those documents is stale, badly chunked, or says the opposite of what the current policy says, every eval run passes and nobody hears about it.

A single split bar across all fifteen documents: ten tested as an answer, five never. A sub-line notes that separately, ten of fifteen appear anywhere in any trace, so the same five are absent entirely. Beneath: a document that loses a comparison has at least been seen, but these five were never even a losing candidate, so they have not been ruled in or ruled out.

It is worse than that, and the second count is the one worth sitting with. Those same five documents never appear in any trace at all, not even as a losing candidate. They have not been ruled in or ruled out. They are simply absent from the evidence.

Absent Is Not the Same as Passing

A document that loses a comparison has been tested. It was a candidate, it scored, and it came second. You know the retriever can see it.

A document that never appears has not been tested at all. You do not know whether it is reachable, whether its chunking is sane, or whether its puts it anywhere near the questions it should answer.

A pass rate cannot tell these apart. Both look like the absence of a problem.

How a Set Ends Up Shaped Like This

A bar chart of cases per document across the whole fifteen document index. Ten bars stand at three cases each and five have no height at all. The note says the eval set is perfectly even across the ten documents it does cover and completely blind to the other five.

Nobody decided to skip five documents. The shape comes from how the set was built.

Cases come from questions. Questions come from what users asked, or from what the team could think of. Both are drawn from the parts of the product people already use.

Documents that answer rare questions therefore get no cases, and documents added after the set was written get none either, because nobody went back.

Two comparison panels on why a document has no case. Rarely read: it answers a question a handful of people ask a year, sampling will almost never pick it, and it is often the document whose answer matters most when it is finally needed. Recently added: the set was written in March and the document arrived in July, so nothing about the eval knows it exists and a missing case produces no failure.

A sequence diagram with three lifelines: ingestion, the index and the eval. Step one, a new policy page is indexed. Step two, nothing is sent to the eval. Step three, the eval runs the same cases and reports the same pass rate. Step four, coverage fell and nobody was told. The note says the eval did not get worse, it got less complete, and a pass rate cannot express the difference.

The set is not wrong. It is a sample of your traffic, and it inherits traffic's shape, including the long tail traffic does not reach.

Run It

The code below joins the traces against the document set. It counts three things. Which documents are a gold answer, which appear anywhere at all, and how the cases are spread across the ones that do get tested.

Section two is the one to sit with. The same five documents appear in both lists, which means they were not beaten by anything. They were never in the room.

Making Coverage a Number You Watch

Two columns either side of a ledger rule. The number you have: thirty cases, describing the effort that went into building the set, and it goes up when you add cases including cases on documents already covered. The number you need: ten of fifteen documents, describing how much of the thing under test the set reaches, and it only goes up when reach does.

Two comparison panels. Today, pass rate only: one number, green, which moves when the system changes and also when the set changes, with nothing distinguishing the two. With coverage, and reach: a second line reading ten of fifteen documents covered with the uncovered ids listed, which moves on its own when a document is added while the pass rate does not.

Report documents covered, next to cases run. Two numbers instead of one. "30 cases over 10 of 15 documents" is a sentence that tells you where you are; "30 cases" is not.

A flowchart. Your index yields the document ids in it; your eval set yields the gold document of each case; both feed a left join, which produces documents with no case. The note says both sides of this join already exist, because the index knows its own ids and every eval case already records which document was supposed to answer it.

A hand-drawn three-step scene. Every document id in the index, which you already have. Left join the gold doc of every case, which you already store per case. Count the rows with no match, which is your uncovered list. A handwritten line reads no model, no labelling, no new runs.

Treat a new document as a case that does not exist yet. When something is added to the index, it starts uncovered. Making that visible at ingest time is cheaper than discovering it during an incident.

A ring gauge reading sixty-seven per cent, labelled ten of fifteen documents covered, with the note that chasing the remaining five is not automatically the right call. Beneath: writing a case for every document in a large index is a poor use of a week, and the useful move is knowing which documents are uncovered and deciding which of them you care about.

Knowledge Check

4 questions - Score 80% to pass

Your eval set has 300 cases and your index has 4,000 documents. What does the case count tell you about coverage?

Five documents never appear in any trace, not even as a losing candidate. What do you know about them?

Why does an eval set built from real traffic tend to leave part of the index uncovered?

What is the right target for index coverage?