Search results for Information-Retrieval

The retrieval rebuild: Why hybrid retrieval intent tripled as enterprise RAG programs hit the scale wall

Something shifted in enterprise RAG in Q1 2026. VB Pulse data spanning January through March tells a consistent story: the market stopped adding retrieval layers and started fixing...

Your AI agents need a terminal, not just a vector database

When agentic workflows fail, developers often assume the problem lies in the underlying model’s reasoning abilities. In reality, the limited information provided by the retrieval i...

I Built a RAG Pipeline. Then I Realized Retrieval Is the Real Model

Everyone talks about the LLM. GPT‑4, Claude, Gemini – that’s the celebrity. But after building my first real RAG pipeline, I learned something humbling: the LLM is the interc...

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 “close...

RAG Is Not Enough: Advanced Retrieval Architectures Using Vertex AI Search on GCP

Retrieval-augmented generation (RAG) caught on fast — and for good reason. Connecting a large language model to your organization's documents feels like the most natural way to bui...

The Eighth Question of AI in IR

Contextual Retrieval: A Practical Guide to Reducing RAG Retrieval Errors by 67%

IntroductionContinue reading on Medium В»

Advanced RAG Retrieval: Cross-Encoders & Reranking

A deep-dive and practical guide to cross-encoders, advanced techniques, and why your retrieval pipeline deserves a second pass. The post Advanced RAG Retrieval: Cross-Encoders &amp...

RAG Done Right: When to Use SQL, Search, and Vector Retrieval and How To Combine Them

In this article, I will attempt to explain why retrieval-agumented generation (RAG) fails when retrieval is treated as a one-size-fits-all approach. For example, the internal AI as...