Of all the facts the right answer needs, how many did retrieval actually fetch? Recall is the ceiling everything else sits under.
💡 In one line: don't miss the good stuff.
Context Recall answers one blunt question: of the facts a correct answer requires, how many actually showed up in the chunks you retrieved? It is the first of the two retrieval metrics in the RAG scorecard, and it is the most important number in the whole pipeline — because a model cannot use a fact it never saw.
That is why we call recall the ceiling. Reranking, prompting, a bigger model — none of it can recover information that retrieval left on the shelf. If recall is 0.6, then 40% of the time the answer the user needs is simply not in the room, and no downstream cleverness invents it.
The intuition: "is it in the pile?"
Picture retrieval dumping a pile of chunks on the desk. Recall asks only one thing: is the needed fact somewhere in that pile? It does not care whether the fact is on top or buried at the bottom — that is precision's job. Recall only cares whether it made it onto the desk at all.
On our IRS tax corpus, the classic recall failure is this: a user asks how to fill in a line on Form 1040, and the real explanation lives in the i1040gi instruction booklet — but retrieval only pulled the bare form. The fact never entered the pile, so recall is where that miss shows up.
How it's scored
RAGAS computes recall against a human-written reference answer, not the model's own opinion (the metric is LLMContextRecall). It breaks the reference into individual claims, then checks each claim: can it be attributed to the retrieved context?
Context Recall = (reference claims supported by retrieved context)
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(total claims in reference)Scored 0..1, higher is better. Grading against the human ground truth is what stops this from being the model marking its own homework.
What good and bad look like
On our hard corpus, naive RAG starts at 0.650 recall — a third of the needed facts simply missing. The fixes that move recall are the ones that widen the pool: query rewriting casts a wider net by rephrasing the question, and contextual retrieval makes chunks easier to find by letting them describe themselves. By the end of the series recall climbs to 0.83.
Note what does not move it: a reranker only reorders the pile it was handed — if the fact was never retrieved, no reordering invents it. When recall is stuck, fix the retrieval, not the ranking.
The trap: recall vs precision
You can always raise recall by retrieving more chunks — fetch 50 instead of 4 and you will catch more needed facts. But now the pile is full of noise, precision collapses, and the model drowns the right answer in junk. The two metrics pull against each other, which is exactly why you measure both. Recall is "is it in the pile?"; precision is "is it on top?".
Part of the Measurements series. Next: Context Precision — is the right chunk ranked high, or buried? See all four metrics working together in how to measure a RAG system.