AI demos often look impressive, delivering fast responses, polished communication, and strong performance in controlled environments. But once real users interact with the system, issues surface like hallucinations, inconsistent tone, and answers that …
When you type a query into a search engine, something has to decide which documents are actually relevant — and how to rank them. BM25 (Best Matching 25), the algorithm powering search engines like Elasticsearch and Lucene, has been the dominant answer to that question for decades. It scores documents by looking at three things: […]
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What if the way we build AI document chatbots today is flawed? Most systems use RAG. They split documents into chunks, create embeddings, and retrieve answers using similarity search. It works in demos but often fails in real use. It misses obvious ans…
Documents are embedded once — worth the spend for maximum quality. Queries hit you on every request. This is what drives your cost at scale. Asymmetric retrieval with Voyage AI and Vespa. Real numbers, real config.
Retrieval-Augmented Generation (RAG) allows an LLM to answer questions using your data at query time. On their own, LLMs are powerful but limited: they can hallucinate, they have a fixed knowledge cutoff, and they know nothing about your private documents, internal wikis, or proprietary systems.
Table of Contents Vector Search Using Ollama for Retrieval-Augmented Generation (RAG) How Vector Search Powers Retrieval-Augmented Generation (RAG) From Search to Context The Flow of Meaning Putting It All Together What Is Retrieval-Augmented Generation (RAG)? The Retrieve-Read-Generate Architecture Explained Why…
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Table of Contents Vector Search with FAISS: Approximate Nearest Neighbor (ANN) Explained From Exact to Approximate: Why Indexing Matters The Trouble with Brute-Force Search The Curse of Dimensionality Enter the Approximate Nearest Neighbor (ANN) Accuracy vs. Latency: The Core Trade-Off…
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Table of Contents TF-IDF vs. Embeddings: From Keywords to Semantic Search Series Preamble: From Text to RAG What You’ll Build Across the Series Project Structure Why Start with Embeddings The Problem with Keyword Search When “Different Words” Mean the Same…
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