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Retrieval-augmented generation grounds language models in external context, but for long documents flat top-$k$ retrieval can cluster on a single region and miss complementary evidence.
I spend a lot of time in the AI space -- reading papers, building things, talking to engineers who are actually shipping. And there is a gap between what the demos show and what production systems actually look like that nobody is being fully honest about.
Why 80% of enterprise RAG failures happen before retrieval starts. Learn the Goldilocks chunking dilemma, 512-token truncation, and production splitting rules.
Complete RAG Engineering Series, Part 6 | A practical guide to HNSW, IVF, product quantization, FAISS, Pinecone, Weaviate, Qdrant, Milvus…Continue reading on Towards AI »
Google LLC today released EmbeddingGemma 2, an open multimodal embedding model small enough to run on a smartphone. The release takes the EmbeddingGemma line beyond text, which was all the first version handled when Google introduced it in September 2025.
Coworker AI explains the context graph: a knowledge graph plus decision traces and time, how it differs from a knowledge graph, and how to build one.
Coworker AI explains the context layer: what it is, how it differs from a semantic layer, RAG and knowledge graphs, and how to build, buy or test one.
Introducing EmbeddingGemma 2, an open multimodal embedding model optimized for privacy-first use cases
A partner agent sent my converter a 46-page supplier agreement and complained the output was useless. The API returned 200 OK and exactly 214 characters for 46 pages: a header, a footer, some page numbers. My first instinct was an extraction bug. It wasn't.
When new information is continually added and old information is removed, an AI system may fail to find the correct material even when that material exists.
Many teams now use LLM-as-a-Judge to check AI answers, especially when exact-match tests fail for long or open-ended responses. But every judgement adds cost, delay, and possible bias, making this hard to scale.
Six ways to collect data for training AI models, plus the problems and benefits of each method.
Can a model that never writes a single word be the brain of an AI agent? We made 21,314 calls to find out, and the...
Learn how AI research agents plan searches, evaluate evidence, synthesize findings, and produce cited reports with human review.
Autonomous driving decision systems must balance safety, efficiency, and social norms in complex traffic interactions. Philosophical and ethical considerations have received limited attention in existing autonomous driving decision-making approaches based on numerical optimization, sequence…
Not every RAG stack needs another database. If your application already runs on PostgreSQL, pgvector may be enough for storing embeddings, filtering results, and running semantic search.
From Retrieval to Governance — The Architecture Shift Replacing Naive RAG Stop asking “How do we retrieve more chunks?” Start asking “How do we govern and structure what the model …
VentureBeat Intelligence surveyed respondents at organizations with 100 or more employees in August 2026. We asked where their AI agents get business context and whether those agents had given wrong answers traced to that context, once or more than once.
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