🐦 Twitter Post Details

Viewing enriched Twitter post

@helloiamleonie

Advanced Retrieval-Augmented Generation (RAG) techniques address the limitations of naive RAG pipelines. A recent survey on RAG classifies advanced RAG techniques into pre-retrieval, retrieval, and post-retrieval optimizations. 🔗 Paper: https://t.co/dWkf0Uc587 My latest article gives an overview of advanced RAG techniques: 🦙 Pre-retrieval includes techniques like sliding windows, enhancing data granularity, adding metadata, or optimizing index structures, such as sentence window retrieval. 🦙 Retrieval includes optimizing the embedding models (e.g., fine-tuning) or advanced retrieval techniques like hybrid search 🦙 Post-retrieval includes reranking or prompt compression. We also implement a naive RAG pipeline using @llama_index and then enhance it to an advanced RAG pipeline using the following: • Sentence window retrieval (as a pre-retrieval optimization) • Hybrid search (as a retrieval optimization) • Re-ranking (as a post-retrieval optimization) 💻 Jupyter Notebooks: https://t.co/MFiz00RQHb Read more on @TDataScience: https://t.co/zgD02G1Rn7

View on Twitter

📊 Media Metadata

{
  "media": [
    {
      "url": "https://pbs.twimg.com/media/GGtiGujX0AA853T.png",
      "type": "photo",
      "original_url": "https://pbs.twimg.com/media/GGtiGujX0AA853T.png",
      "format_converted_from_list": true
    }
  ],
  "conversion_date": "2025-08-13T00:32:20.158570",
  "format_converted": true,
  "original_structure": "had_media_only"
}

🔧 Raw API Response

{
  "user": {
    "created_at": "2022-08-16T09:02:34.000Z",
    "default_profile_image": false,
    "description": "🖋️ Technical writer https://t.co/mXXEyoAUJZ\n🥑 Developer Advocate @weaviate_io\n🦆 Kaggle Notebooks Grandmaster https://t.co/3wrvdColIa",
    "fast_followers_count": 0,
    "favourites_count": 5733,
    "followers_count": 6068,
    "friends_count": 529,
    "has_custom_timelines": true,
    "is_translator": false,
    "listed_count": 96,
    "location": "🇩🇪🇯🇵",
    "media_count": 135,
    "name": "Leonie",
    "normal_followers_count": 6068,
    "possibly_sensitive": false,
    "profile_banner_url": "https://pbs.twimg.com/profile_banners/1559465561796579328/1701685760",
    "profile_image_url_https": "https://pbs.twimg.com/profile_images/1559465709008297985/1Q_wkd4i_normal.jpg",
    "screen_name": "helloiamleonie",
    "statuses_count": 1876,
    "translator_type": "none",
    "url": "https://t.co/RNC0wHELH6",
    "verified": true,
    "withheld_in_countries": [],
    "id_str": "1559465561796579328"
  },
  "id": "1759608020114211323",
  "conversation_id": "1759608020114211323",
  "full_text": "Advanced Retrieval-Augmented Generation (RAG) techniques address the limitations of naive RAG pipelines.  \n\nA recent survey on RAG classifies advanced RAG techniques into pre-retrieval, retrieval, and post-retrieval optimizations. \n🔗 Paper: https://t.co/dWkf0Uc587 \n\nMy latest article gives an overview of advanced RAG techniques:  \n\n🦙 Pre-retrieval includes techniques like sliding windows, enhancing data granularity, adding metadata, or optimizing index structures, such as sentence window retrieval.  \n\n🦙 Retrieval includes optimizing the embedding models (e.g., fine-tuning) or advanced retrieval techniques like hybrid search  \n\n🦙 Post-retrieval includes reranking or prompt compression.  \n\nWe also implement a naive RAG pipeline using @llama_index and then enhance it to an advanced RAG pipeline using the following: \n• Sentence window retrieval (as a pre-retrieval optimization) \n• Hybrid search (as a retrieval optimization) \n• Re-ranking (as a post-retrieval optimization)  \n\n💻 Jupyter Notebooks: https://t.co/MFiz00RQHb \n\nRead more on @TDataScience: https://t.co/zgD02G1Rn7",
  "reply_count": 13,
  "retweet_count": 142,
  "favorite_count": 787,
  "hashtags": [],
  "symbols": [],
  "user_mentions": [],
  "urls": [
    {
      "url": "https://t.co/yYz6tuk4Re",
      "expanded_url": "https://arxiv.org/pdf/2312.10997.pdf",
      "display_url": "arxiv.org/pdf/2312.10997…"
    }
  ],
  "media": [
    {
      "media_url": "https://pbs.twimg.com/media/GGtiGujX0AA853T.png",
      "type": "photo"
    }
  ],
  "url": "https://twitter.com/helloiamleonie/status/1759608020114211323",
  "created_at": "2024-02-19T15:56:58.000Z",
  "#sort_index": "1759608020114211323",
  "view_count": 111540,
  "quote_count": 15,
  "is_quote_tweet": false,
  "is_retweet": false,
  "is_pinned": false,
  "is_truncated": true,
  "startUrl": "https://twitter.com/helloiamleonie/status/1759608020114211323"
}