Weaviate introduces configurable recall-versus-precision tradeoff for its Query Agent Search Mode.

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  "detected_at": "2026-08-13T14:26:38.976+00:00",
  "company": {
    "name": "Weaviate",
    "domain": "weaviate.io"
  },
  "data": {
    "tags": [
      "Search",
      "Analytics & Insights",
      "Information Technology",
      "Product Development",
      "Database"
    ],
    "summary": "Weaviate introduces configurable recall-versus-precision tradeoff for its Query Agent Search Mode.",
    "post_url": "https://www.linkedin.com/posts/weaviate-io_query-agent-search-mode-now-makes-the-recall-versus-precision-activity-7493305696915103745-z247",
    "num_likes": 44,
    "post_text": "Query Agent Search Mode now makes the recall-versus-precision tradeoff configurable.\n\nSearch Mode rewrites a natural-language request into one or multiple Weaviate queries, each containing a search query, metadata filters, or both. It then returns the matching Weaviate objects directly.\n\nThe new 𝗳𝗶𝗹𝘁𝗲𝗿𝗶𝗻𝗴 argument controls the search strategy:\n\n- \"𝘳𝘦𝘤𝘢𝘭𝘭\" (default) generates multiple queries spanning different filters and interpretations. Use it when getting relevant results matters more than satisfying each criteria.\n- \"𝘱𝘳𝘦𝘤𝘪𝘴𝘪𝘰𝘯\" generates a single query targeting the most likely interpretation. Use it when every returned result should closely follow the original intent.\n\nImagine an ecommerce dataset with 𝘤𝘢𝘵𝘦𝘨𝘰𝘳𝘺, 𝘮𝘢𝘵𝘦𝘳𝘪𝘢𝘭, 𝘸𝘢𝘵𝘦𝘳𝘱𝘳𝘰𝘰𝘧, and 𝘱𝘳𝘪𝘤𝘦 fields. For\n\n𝘍𝘪𝘯𝘥 𝘮𝘦 𝘸𝘢𝘵𝘦𝘳𝘱𝘳𝘰𝘰𝘧 𝘳𝘦𝘤𝘺𝘤𝘭𝘦𝘥 𝘱𝘰𝘭𝘺𝘦𝘴𝘵𝘦𝘳 𝘣𝘰𝘰𝘵𝘴 𝘧𝘰𝘳 𝘸𝘪𝘯𝘵𝘦𝘳 𝘶𝘯𝘥𝘦𝘳 $150\n\nThe \"recall\" method might perform a search on 𝘸𝘪𝘯𝘵𝘦𝘳 𝘣𝘰𝘰𝘵𝘴 with filters on 𝘮𝘢𝘵𝘦𝘳𝘪𝘢𝘭, 𝘸𝘢𝘵𝘦𝘳𝘱𝘳𝘰𝘰𝘧 and 𝘱𝘳𝘪𝘤𝘦. Then run backup searches such as 𝘸𝘢𝘵𝘦𝘳𝘱𝘳𝘰𝘰𝘧 𝘳𝘦𝘤𝘺𝘤𝘭𝘦𝘥 𝘱𝘰𝘭𝘺𝘦𝘴𝘵𝘦𝘳 𝘣𝘰𝘰𝘵𝘴 with only 𝘱𝘳𝘪𝘤𝘦<150.\n\nThe \"precision\" method would run one query for 𝘸𝘪𝘯𝘵𝘦𝘳 𝘣𝘰𝘰𝘵𝘴 with all filters applied, returning nothing if there is no exact match.\n\nRead the...",
    "initiatives": [
      {
        "topic": "enhancing search query interpretation",
        "urgency": 0.6
      }
    ],
    "posted_date": "2026-08-12T14:01:37.032Z",
    "num_comments": 8,
    "technologies_mentioned": [
      {
        "name": "Weaviate",
        "status": "using"
      }
    ]
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curl https://signals.autobound.ai/v1/signals/b73c5d3c-af72-4b37-9bee-69966ddd0980 \
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curl -X POST https://signals.autobound.ai/v1/companies/enrich \
  -H "X-API-KEY: $AUTOBOUND_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"domain":"weaviate.io","limit":20}'