#Retrieval

Building a basic Retrieval-Augmented Generation (RAG) prototype is a weekend project. You pip install...

My RAG system for financial document Q&A was stuck at 53% accuracy. I spent two weeks...

If you’ve built a RAG (Retrieval-Augmented Generation) system in the past year, you’ve probably hit the wall where your LLM returns confidently wrong answers, cites information that doesn’t exist, or

Local LLM knowledge base giving bad answers? The fix is almost always the retrieval layer. How to debug chunking, embeddings, and reranking.

From Dev.to - beginners: Building a Personal AI Second Brain: Architecture Decisions That Actually Mattered

From Dev Community: The End of the Memory Tax: How Google’s TurboQuant is Rewriting the Rules of Local RAG Systems

Enterprise AI systems are entering a phase where inference design matters as much as model capability itself.

Retrieval-augmented generation is excellent for knowledge lookup. It is the wrong tool for enforcing architectural decisions. The distinction is deep, and most teams building AI coding workflows haven't confronted it yet.

I built a RAG system that scored 91% on our internal eval suite. It retrieved the right chunks four...

How to combine BM25 lexical retrieval with dense-vector search using Reciprocal Rank Fusion — the fix for the majority of RAG failures that happen before the LL

Beyond Basic RAG: The Rise of Agentic Retrieval Retrieval-Augmented Generation (RAG) has...

When I set out to evaluate retrieval configurations for Portuguese clinical text, I expected one...

Modern retrieval systems, whether lexical or semantic, expose a corpus through a fixed similarity interface that compresses access into a single top-k retrieval step before reasoning.…

Optimize Retrieval-Augmented Generation pipelines: chunking strategies, embedding selection, retrieval tuning, and evaluation.

Optimize RAG retrieval with hybrid search combining dense and sparse methods, cross-encoder re-ranking, and query transformation techniques for better results.

A practical guide to evaluating RAG systems: retrieval metrics, generation quality assessment, end-to-end testing frameworks, and benchmark datasets.

From Dev.to - ai: RetrievalOps | Azure AI Search Relevance Engineering | A R.A.H.S.I. Framework™ Analysis

The AI Engineer Illusion: Why Calling LLM APIs Is Not Enough Three engineers interviewed...

Most discussions about local AI focus on one thing: Can the language model run locally? That...

From Dev.to - ai: PromptOps | Claude PromptOps on Microsoft Foundry | R.A.H.S.I. Framework™