Exact searches are great until they are not. If your results are too narrow, try widening the net with Phrase Search. Converting an entity into a phrase can surface similar or approximate matches, including alternate aliases that exact searches might miss. Just remember, broader results work best when paired with thoughtful filtering.
Widen Your Search with Phrase Search on LinkedIn
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Most marketers look at search term reports one query at a time. That works until you’re dealing with thousands of rows and no clear patterns. N-gram analysis helps break search queries into recurring word combinations. Less digging. More signal. Use this tool to make search term analysis faster and actually useful. https://lnkd.in/dMbU5j54
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Today’s Eureka moment from Anthropic’s Building with the Claude API course: RRF — Reciprocal Rank Fusion ✨ https://lnkd.in/gpfuz6x5 I learned that when combining BM25 keyword search with semantic search, we shouldn’t just mash their scores together and hope for the best. Why? Their scores speak totally different “languages.” BM25 might say “8.7,” semantic search might say “0.82,” and comparing them directly can get messy very fast. RRF takes a much cuter approach: Instead of asking, “what’s your score?”, It asks: “Where did you rank?” 🏅 Formula: score(d) = Σ 1 / (k + rank(d)) Simple idea, big impact. By fusing rankings instead of raw scores, RRF makes hybrid search more stable, practical, and easier to use in RAG systems. Tiny formula, surprisingly powerful little helper. 🤖
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🔍 Improving the Retriever in My RAG Pipeline I started with a standard similarity search setup for my RAG system. It worked — but like most real-world cases, it had its limitations. So I experimented with two additional approaches: 👉 Keyword (lexical) search 👉 Hybrid search (combining both worlds) ⚙️ What’s happening under the hood? Similarity Search Uses embeddings + ANN to find semantically similar content in vector space. Great for understanding meaning, but can miss exact terms (e.g., IDs, keywords). Keyword Search Traditional lexical matching. Perfect for exact matches, but lacks semantic understanding. 🚀 Hybrid Search = Best of Both Hybrid search runs both approaches in parallel and combines their scores/rankings to return better results. 📊 My Observation After introducing hybrid search: Retrieval quality improved (more relevant results surfaced) Edge cases with exact terms were handled better However, one important trade-off emerged: 👉 Increased latency Since both searches run together and results need to be merged/ranked, the response time can take a hit. 💡 Key Takeaway Hybrid search improves recall and robustness — but you need to carefully balance it with latency. 🔍 What I’m Exploring Next Smarter ranking strategies Selective hybrid search (only when needed) Rerankers to optimize final results
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Stop searching for similarity. Start searching for relevance. Traditional RAG has a flaw: vector similarity ≠ logical relevance. Nearest-neighbor retrieval often returns context that looks related — but misses the actual answer. That’s why reasoning-based, vectorless RAG frameworks like "PageIndex" are gaining attention: • No Vector DB overhead • Better context preservation • Retrieval driven by reasoning, not just embedding proximity If your RAG pipeline keeps returning “close enough” results, retrieval — not the LLM — may be the real bottleneck. #AIEngineering #RAG #LLMs #VectorDB #PageIndex
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𝗥𝗔𝗚 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗩𝗲𝗰𝘁𝗼𝗿𝘀: 𝗛𝗼𝘄 𝗣𝗮𝗴𝗲𝗜𝗻𝗱𝗲𝘅 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗲𝘀 𝗯𝘆 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 Retrieval is where most RAG systems quietly break. Traditional pipelines rely on vector similarity—embedding queries and document chunks into the same space and fetching the “closest” matches. But similarity is a weak proxy for what we actually need: relevance grounded in reasoning. In long, professional documents—like financial reports, research papers, or legal texts—the right answer […] The post RAG Without Vectors: How PageIndex Retrieves by Reasoning appeared first on MarkTechPost. https://lnkd.in/ehJYs5hQ
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Keyword overlap is easy. Useful results are harder. A paper can contain the right terms and still be wrong for the job. Topic fit, context, study type, and actual usefulness matter more than string matching. That's the difference between a search result and a reading list. Build a first digest here: https://lnkd.in/edRcpmvC
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#chunkoverlop 🔹 Why Overlap is Important Without overlap: ❌ Meaning breaks at chunk edges ❌ Retrieval may miss key phrases ❌ LLM gets incomplete context With overlap: ✅ Better semantic continuity ✅ Higher recall in vector search ✅ More accurate answers