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How do I interpret ranking score details?

In the previous guide, we covered how ranking rules determine result order and how changing their sequence affects what your users see first. But when you’re actually making those tweaks, how do you know if they’re working the way you expect? That’s where ranking score details come in. They give you a behind-the-scenes view of every ranking decision Meilisearch made for each result — with specific numeric scores for each relevancy rule, in the order they were evaluated. You’ll be able to see things like: did Proximity decide this result’s position, or was it Typo? Did Sort even get a chance to act, or did an earlier rule already settle things? And since Sort doesn’t measure relevance (it shows a value rather than a score), the details also make it clear exactly where Sort slotted into the evaluation path and whether it actually influenced the final order. Firstly, how do I see ranking score details? When you search you can pass in an option to view the details of scoring and sorting using “showRankingScoreDetails”: true and it will return an indepth look at the ranking rules that you are working with 
curl \
  -X POST 'MEILISEARCH_URL/indexes/movies/search' \
  -H 'Content-Type: application/json' \
  --data-binary '{
    "q": "dragon",
    "showRankingScoreDetails": true
  }'
Ranking Score details example 
{
  "hits": [
    {
      "id": 31072,
      "title": "Dragon",
      "overview": "In a desperate attempt to save her kingdom…",
      …
      "_rankingScoreDetails": {
        "words": {
          "order": 0,
          "matchingWords": 4,
          "maxMatchingWords": 4,
          "score": 1.0
        },
        "typo": {
          "order": 2,
          "typoCount": 1,
          "maxTypoCount": 4,
          "score": 0.75
        },
        "name:asc": {
          "order": 1,
          "value": "Dragon"
        }
      }
    },
    …
  ],
  …
}

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Ranking rules: same data, different results. How sort placement changes outcomes

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The setup

You run a recipe search app. You have two recipes in your index: 
[
  {
    "id": 1,
    "title": "Easy Chicken Curry",
    "description": "A quick and simple chicken curry ready in 20 minutes",
    "prep_time_minutes": 20
  },
  {
    "id": 2,
    "title": "Chicken Stew with Curry Spices and Vegetables",
    "description": "A hearty stew with warming spices",
    "prep_time_minutes": 15
  }
]
A user searches for "chicken curry" and sorts by prep_time_minutes:asc (quickest first). Both documents match both search words. But Doc 1 is clearly the stronger text match as "chicken" and "curry" appear right next to each other in the title. Doc 2 has both words in the title too, but they’re separated by several other words. Let’s see how moving Sort one position in your ranking rules changes which result comes first, and how to read the ranking score details to understand why.
We’ve set up our ranking rules to have sort after our Group 1 wide net rules. 
["words", "typo", "proximity", "sort", "attribute", "exactness"]
With this set up Meilisearch evaluates the text relevance rules first, then uses Sort.

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🥇 Result #1 — Easy Chicken Curry

{
      "prep_time_minutes": 20,
      "title": "Easy Chicken Curry",
      "id": 1,
      "description": "A quick and simple chicken curry ready in 20 minutes",
      "_rankingScore": 0.9982363315696648,
      "_rankingScoreDetails": {
        "words": {
          "order": 0,
          "matchingWords": 2,
          "maxMatchingWords": 2,
          "score": 1.0
        },
        "typo": { "order": 1, "typoCount": 0, "maxTypoCount": 2, "score": 1.0 },
        "proximity": { "order": 2, "score": 1.0 },
        "prep_time_minutes:asc": { "order": 3, "value": 20.0 },
        "attribute": {
          "order": 4,
          "attributeRankingOrderScore": 1.0,
          "queryWordDistanceScore": 0.9047619047619048,
          "score": 0.9682539682539683
        },
        "exactness": {
          "order": 5,
          "matchType": "noExactMatch",
          "matchingWords": 2,
          "maxMatchingWords": 2,
          "score": 0.3333333333333333
        }
      }
    }

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🥈 Result #2 — Chicken Stew with Curry Spices and Vegetables

{
      "prep_time_minutes": 15,
      "title": "Chicken Stew with Curry Spices and Vegetables",
      "id": 2,
      "description": "A hearty stew with warming spices",
      "_rankingScore": 0.9149029982363316,
      "_rankingScoreDetails": {
        "words": {
          "order": 0,
          "matchingWords": 2,
          "maxMatchingWords": 2,
          "score": 1.0
        },
        "typo": { "order": 1, "typoCount": 0, "maxTypoCount": 2, "score": 1.0 },
        "proximity": { "order": 2, "score": 0.5 },
        "prep_time_minutes:asc": { "order": 3, "value": 15.0 },
        "attribute": {
          "order": 4,
          "attributeRankingOrderScore": 1.0,
          "queryWordDistanceScore": 0.9047619047619048,
          "score": 0.9682539682539683
        },
        "exactness": {
          "order": 5,
          "matchType": "noExactMatch",
          "matchingWords": 2,
          "maxMatchingWords": 2,
          "score": 0.3333333333333333
        }
      }

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What decided this? Reading the score details

Walk through the rules in order (0, 1, 2…) and look for where the scores diverge:
StepRuleDoc 1Doc 2Outcome
0Words2/2 → 1.02/2 → 1.0🤝 Tie
1Typo0 typos → 1.00 typos → 1.0🤝 Tie
2Proximity1.00.5✅ Doc 1 wins here
Proximity broke the tie. "chicken" and "curry" sit right next to each other in Doc 1’s title (score 1.0), but are separated by three words in Doc 2’s title (score 0.5). Sort (order 3) never got a chance to act because Proximity already decided the winner. Even though Doc 2 has a faster prep time (15 min vs 20 min), it ranks second because text relevance was evaluated first. Also notice: Sort shows a value instead of a score. That’s because Sort doesn’t measure relevance, it just orders by the field value. This is why Sort doesn’t contribute to _rankingScore.

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Scenario B: sort placed BEFORE Group 1 rules

Now let’s move sort to the top of our ranking rules: 
["sort", "words", "typo", "proximity", "attribute", "exactness"]

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🥇 Result #1 — Chicken Stew with Curry Spices and Vegetables

 {
      "prep_time_minutes": 15,
      "title": "Chicken Stew with Curry Spices and Vegetables",
      "id": 2,
      "description": "A hearty stew with warming spices",
      "_rankingScore": 0.9149029982363316,
      "_rankingScoreDetails": {
        "prep_time_minutes:asc": { "order": 0, "value": 15.0 },
        "words": {
          "order": 1,
          "matchingWords": 2,
          "maxMatchingWords": 2,
          "score": 1.0
        },
        "typo": { "order": 2, "typoCount": 0, "maxTypoCount": 2, "score": 1.0 },
        "proximity": { "order": 3, "score": 0.5 },
        "attribute": {
          "order": 4,
          "attributeRankingOrderScore": 1.0,
          "queryWordDistanceScore": 0.9047619047619048,
          "score": 0.9682539682539683
        },
        "exactness": {
          "order": 5,
          "matchType": "noExactMatch",
          "matchingWords": 2,
          "maxMatchingWords": 2,
          "score": 0.3333333333333333
        }
      }
  }

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🥈 Result #2 — Easy Chicken Curry

{
      "prep_time_minutes": 20,
      "title": "Easy Chicken Curry",
      "id": 1,
      "description": "A quick and simple chicken curry ready in 20 minutes",
      "_rankingScore": 0.9982363315696648,
      "_rankingScoreDetails": {
        "prep_time_minutes:asc": { "order": 0, "value": 20.0 },
        "words": {
          "order": 1,
          "matchingWords": 2,
          "maxMatchingWords": 2,
          "score": 1.0
        },
        "typo": { "order": 2, "typoCount": 0, "maxTypoCount": 2, "score": 1.0 },
        "proximity": { "order": 3, "score": 1.0 },
        "attribute": {
          "order": 4,
          "attributeRankingOrderScore": 1.0,
          "queryWordDistanceScore": 0.9047619047619048,
          "score": 0.9682539682539683
        },
        "exactness": {
          "order": 5,
          "matchType": "noExactMatch",
          "matchingWords": 2,
          "maxMatchingWords": 2,
          "score": 0.3333333333333333
        }
      }
 }

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Reading the score details - what changed?

Look at the order values. Sort is now order: 0 so it runs first.
StepRuleDoc 1 (Easy Chicken Curry)Doc 2 (Chicken Stew…)Outcome
0Sort (prep_time_minutes:asc)value: 20value: 15✅ Doc 2 wins here
Sort immediately separated the documents: 15 min beats 20 min. :asc will sort lowest to highest. Words, Typo, Proximity, and the rest never got a say. Notice something important: Doc 1 still has a higher _rankingScore (0.998 vs 0.914) but it ranks second. This is exactly what we described in Ordering ranking rules: ranking score only measures text relevance. Sort affects the final order but doesn’t change the ranking score. If you only looked at _rankingScore, you’d think Doc 1 should be first. The score details tell you the real story.

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Side by side

In both scenarios the user searches for "chicken curry" and sorts by prep_time_minutes:asc (quickest first). The only change is the ranking rule placement.
Scenario A (Sort is placed after Group 1 ranking rules)Scenario B (Sort is placed first)
#1 resultEasy Chicken Curry (20 min)Chicken Stew with Curry… (15 min)
Decided byProximity (order 2)Sort (order 0)
Doc 1 _rankingScore0.9980.998 (same — sort doesn’t affect it)
Doc 2 _rankingScore0.9140.914(same — sort doesn’t affect it)
Best forUsers who want the most relevant recipeUsers who want the quickest recipe regardless of match quality

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The takeaway

Moving Sort one position flipped the results. The ranking score details let you see exactly why:
  • Look at the order values to understand the sequence rules were applied
  • Find where scores first diverge — that’s the rule that decided the final order
  • Remember that Sort shows a value, not a score It doesn’t contribute to _rankingScore, which is why a higher-scored document can rank lower when Sort takes priority
Start with Sort after Group 1 rules (Scenario A) and adjust from there based on what your users expect.