A team running a customer support RAG bot over two million internal documents found that a small cluster of repeated queries — such as "reset password" and its variants — accounted for 38% of all vect…
A developer's guide to RAG chunking argues that the document-splitting step is the most commonly rushed and most damaging part of a retrieval pipeline, since errors there propagate downstream into emb…
AI Gateway's model leaderboard for the period from June 21, 2026 to September 18, 2026 shows Jev leading in reach at 15.6% of teams and in preference at 13.3% of teams using it as their primary model …
A developer explains embeddings as the mechanism that lets search match text with no shared words, such as linking "how do I reset my password" to a page titled "account recovery." The walkthrough des…
A developer demonstrated how to add semantic search to an existing Next.js todo app by storing OpenAI text-embedding-3-small vectors in a Postgres column via the pgvector extension, then ranking rows …
Developer Avinoth built a miniature Perplexity clone, called minilexity, that reproduces the AI search engine's retrieval-augmented generation pipeline using Tavily for search, OpenAI's text-embedding…
Ragas 0.4.3 introduces an evaluation harness that scores RAG pipeline outputs on faithfulness, context precision, and answer relevancy, using LLM judges to identify whether failures originate from the…
A technical guide on building retrieval-augmented generation (RAG) pipelines for current events compares major news APIs, noting that NewsAPI lacks deep semantic understanding, while Bing News Search …
A feature built by Soamee for a SaaS client, costing less than $30 per month to run, reduced support tickets by 40% in the first eight weeks. The system uses basic RAG with OpenAI's text-embedding-3-s…
A developer's investigation into InboxSync, a RAG-based email reply system, revealed that its confidence score is meaningless: every query, including spam, out-of-office auto-replies, and GDPR legal r…
Retrieval-Augmented Generation (RAG) is the key to building AI agents that don't hallucinate, according to a new guide that outlines how to set up retrieval layers using vector stores like Chroma, FAI…
A new experiment by Dylan Castillo comparing Matryoshka Representation Learning (MRL) and Principal Component Analysis (PCA) for reducing embedding dimensions found that both methods preserve retrieva…
A developer has built a RAG-powered database assistant using PostgreSQL and pgvector, enabling natural-language queries to be answered in seconds. The system retrieves schema metadata via vector simil…
A developer building a RAG-powered documentation bot for a TypeScript framework found that the bot kept hallucinating outdated API docs because vector search retrieved high-similarity chunks from old …
A developer argues that vector databases cannot distinguish between current and deprecated document versions, citing a gross margin example where two nearly identical definitions yield cosine similari…
A developer rebuilt a retrieval-augmented generation (RAG) pipeline from first principles, achieving 95% recall@10 and cutting latency by 40% through a combination of document-type-aware chunking stra…
A developer's tutorial explains the complete architecture of vector databases, covering schema design, indexing methods like IVF and HNSW, product quantization, distance metrics, metadata indexing, an…
A developer built an AI-powered reverse hiring platform using Next.js, Supabase, and OpenAI. The platform flips the traditional hiring model by requiring companies to find candidates, disclosing salar…
A tutorial shows developers how to give AI agents persistent long-term memory using Postgres with the pgvector extension and OpenAI embeddings, eliminating the need for external vector databases. The …
A developer tested HyDE (Hypothetical Document Embeddings) against standard retrieval on three query types and found it improved conceptual searches but failed on internal company policy and exact pro…