#RAG

Dev.to tutorial May 11

RAG is Not Dead - It’s Just Becoming Agent Memory

RAG is not dead. It just got promoted. For years, retrieval-augmented generation helped apps pull the...

RAG

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Dev.to tutorial May 11

Agents that monitor themselves: a self-auditing RAG on Tiger's Agentic Postgres

An experiment in giving an LLM agent the SQL primitives to watch its own retrieval quality. We build a tiny RAG on Tiger's Agentic Postgres stack, then expose ragvitals' drift dime...

RAG Agents

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Dev.to tutorial May 11

Fully open-source RAG with pgvector + pgai + Ollama, and ragvitals watching for drift

An end-to-end open-source RAG stack on Postgres: pgvector for storage, pgai for embedding and generation inside SQL, Ollama for serving Gemma 2 and Llama 3.1 locally, and a 5-dimen...

Google RAG Open Source

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Dev.to tutorial May 11

RAG Series (13): Query Optimization — Asking Better Questions

The Same Question, Completely Different Results Vector retrieval has a fragility that's...

RAG

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Mastodon discussion May 11

Traditional RAG works for simple lookups, but supply chain leaders need AI that can plan, evaluate, and synthesize evide...

Traditional RAG works for simple lookups, but supply chain leaders need AI that can plan, evaluate, and synthesize evidence. https://hackernoon.com/beyond-chat-why-enterprise-suppl...

RAG

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Dev.to tutorial May 10

Why Your RAG Chatbot Looks Great in Week 1 and Hallucinates by Month 2

💡 Week 1 demo → "this is amazing." Month 2 production → "why is it hallucinating?" I've seen this...

RAG

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Dev.to tutorial May 10

Beyond SQL: How to Build a High-Performance On-Device Vector Search Engine for Android

In the traditional world of Android development, we’ve spent decades perfecting the art of the exact...

RAG

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Dev.to tutorial May 10

Building a multi-tenant RAG pipeline with Postgres. Part 0: Overview

Today I want to start with a series of articles describing my experience building a multi-tenant RAG...

RAG

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Mastodon discussion May 10

Avi Chawla (@_avichawla)프롬프트 엔지니어링, RAG, 컨텍스트 엔지니어링, 파인튜닝, 에이전트, LLM 배포/최적화, 안전성·평가·관측성까지 포함한 풀스택 AI 엔지니어링 로드맵을 소개합니다. 무...

Avi Chawla (@_avichawla)프롬프트 엔지니어링, RAG, 컨텍스트 엔지니어링, 파인튜닝, 에이전트, LLM 배포/최적화, 안전성·평가·관측성까지 포함한 풀스택 AI 엔지니어링 로드맵을 소개합니다. 무료 오픈소스 자료도 함께 제공되어 AI 개발자에게 유용합니다.https://x.com/_avichawla/s...

LLM RAG

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Mastodon discussion May 10

TechCrunch drops a massive new glossary to define complex terms like RAG, RLHF, and Large Language Models for everyday u...

TechCrunch drops a massive new glossary to define complex terms like RAG, RLHF, and Large Language Models for everyday users. The tech industry is pushing for total AI literacy in ...

RAG

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Dev.to tutorial May 10

Most RAG failures don’t crash. They silently return bad answers. I built a repair layer for that.

Most RAG tooling provides a score but fails to specify what actually went wrong. I had retrieval...

RAG

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Dev.to tutorial May 10

Generation 2 — RAG-Augmented Models (2022–2023)

Generation 2: RAG — The Era of Grounded Knowledge (2022–2023) In the first generation of AI, models...

RAG

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Mastodon discussion May 10

Blockify Cuts RAG Corpus by 40x, Boosts Retrieval 2.3xBlockify claims 40x corpus reduction and 2.3x relevance gain over ...

Blockify Cuts RAG Corpus by 40x, Boosts Retrieval 2.3xBlockify claims 40x corpus reduction and 2.3x relevance gain over naive RAG. Open-source on GitHub, but lacks benchmark detail...

RAG

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Dev.to tutorial May 9

Your RAG can't answer 'why' -- GraphRAG finds what vector search misses

The question that broke my RAG pipeline I had a solid RAG setup. Embeddings, vector store,...

RAG

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Dev.to tutorial May 9

Beyond Vector Search: Why GraphRAG is the Next Frontier for LLMs

Beyond Vector Search: Why GraphRAG is the Next Frontier for LLMs For the past year, the...

RAG

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Dev.to tutorial May 9

Graphs for RAG: Knowledge Graph and GraphRAG (GraphDB)

Introduction I introduced RAG for LLM inference in the previous post in this series. As I...

RAG

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Dev.to tutorial May 9

A 19th Century Author Taught Me RAG.

I asked a 14 billion parameter LLM to remember a short story by Nathaniel Hawthorne and it told me it...

RAG

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Mastodon discussion May 9

Show HN: Nexa-gauge – Cache/cost-aware graph-based eval for LLM and RAGNexa-gauge는 LLM, RAG, 에이전트 시스템의 생성 결과를 평가하기 위한 파이...

Show HN: Nexa-gauge – Cache/cost-aware graph-based eval for LLM and RAGNexa-gauge는 LLM, RAG, 에이전트 시스템의 생성 결과를 평가하기 위한 파이썬 패키지이자 CLI 도구로, 그래프 기반 평가 파이프라인을 통해 반복 가능한 품질 지표와 비용 추정, 캐시...

LLM RAG Benchmark

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Dev.to tutorial May 9

Small-to-Big RAG: Your AI Needs a Better Context 🧠

If your text chunks are too small, the AI misses the context. If they are too big, the search becomes...

RAG

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Dev.to tutorial May 9

You're doing RAG wrong

There's a new approach that: cuts corpus size by 40x. reduces tokens per query by 3x. improves vector...

RAG

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Mastodon discussion May 9

Engineering Intelligence from Autocomplete이 글은 LLM이 단순한 다음 단어 예측기임에도 불구하고, 적절한 제약 조건(프롬프트, RAG, 도구 사용, 온도 조절)을 통해 복잡한 문제...

Engineering Intelligence from Autocomplete이 글은 LLM이 단순한 다음 단어 예측기임에도 불구하고, 적절한 제약 조건(프롬프트, RAG, 도구 사용, 온도 조절)을 통해 복잡한 문제 해결이 가능해지는 원리를 설명한다. 특히 챗봇 구현 시 LLM이 상태를 기억하지 않으므로 애플리케이션이 대...

OpenAI Anthropic LLM

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Mastodon discussion May 9

S Banerjee (@SB434223)RAG에서 임베딩 품질만으로는 충분하지 않으며, 데이터가 커질수록 검색 공간이 조밀해져 ‘거의 관련 있는’ 문서가 늘고 recall이 떨어진다는 점을 강조한다. 따라서 대규모 ...

S Banerjee (@SB434223)RAG에서 임베딩 품질만으로는 충분하지 않으며, 데이터가 커질수록 검색 공간이 조밀해져 ‘거의 관련 있는’ 문서가 늘고 recall이 떨어진다는 점을 강조한다. 따라서 대규모 RAG에서는 reranking 같은 후처리와 검색 설계가 중요하다는 기술적 인사이트를 제시한다.https:/...

RAG

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Dev.to tutorial May 9

Stop Guessing Your RAG Quality: Automating Faithfulness Metrics with Spring AI and LLM-as-a-Judge

Stop Shipping Hallucinations: Automating RAG Faithfulness with Spring AI 1.2 If you’re...

LLM RAG

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Dev.to tutorial May 9

Vector Database Là Gì? Giải Mã "Trái Tim" Của Kỷ Nguyên AI

Chào anh em Developer! Trong bối cảnh Trí tuệ Nhân tạo (AI) và Học máy (ML) bùng nổ, việc xử lý dữ...

RAG

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