Tag: #rag

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• Open Source Worth Watching Right Now (1) AI Agents and LLM Tooling

  The LLM application stack has split into layers: inference servers, orchestration, gateways, agents, and RAG. This post introduces 12 open source projects that are actually used at each layer, grouped by role rather than

  2026-08-12 · 6 min read #open-source#llm#ai-agent#ai-platform#rag

• Korean Dev Blog Curation 3 — AI and ML in Practice, 14 Posts I Opened and Checked

  Fourteen Korean-language posts on applying AI and ML in production, selected for being concrete and reproducible. They cover an end-to-end LangChain RAG pipeline, how semantic search actually works through embeddings and

  2026-08-12 · 12 min read #curation#큐레이션#ai#llm#rag

• Embeddings and Rerankers: What Actually Matters in RAG

  What decides your embedding model in a RAG build is not a leaderboard rank but dimensionality, maximum input length, prefix conventions, multilingual coverage, and the division of labor with a reranker. This post lays ou

  2026-08-12 · 7 min read #ai#llm#huggingface#open-source-llm#embedding

• Building an Internal Knowledge Base on an LLM — Permission-Aware Retrieval, Freshness, and the Eval Set You Need Before Launch

  The account Cerebras published on July 15, 2026 of how they built their internal knowledge base exposes the internals of a system fielding more than 15,000 queries a day from humans, automation, and agents alike. But the

  2026-07-31 · 14 min read #ai#rag#enterprise-search#llm#platform-engineering

• LLM API Cost Optimization — Break-Even Math for Output Tokens, Prompt Caching and Routing

  Cutting an LLM API bill in half is arithmetic, not intuition. Because output tokens cost several times what input tokens cost, the biggest lever is almost always controlling output length, with prompt caching next. This

  2026-07-26 · 12 min read #llm#cost-optimization#prompt-caching#rag#model-routing

• When RAG Answers the Wrong Thing — a Debugging Procedure That Separates Retrieval Failure from Generation Failure

  When RAG returns a wrong answer, most teams start by fixing the prompt, but the majority of real causes sit in the retrieval stage. This post starts with the single experiment — hand the model the gold chunk directly — t

  2026-07-26 · 13 min read #llm#rag#retrieval#chunking#evaluation

• RAG · Fine-Tuning · Long-Context — Which One for My Problem: What the Papers Actually Measured, and What No One Measured ♪ Listenable

  It's the most common question in LLM architecture, yet most answers are decision trees with no sources. This post answers only with what has been measured. That fine-tuning fails at injecting new knowledge has been measu

  2026-07-17 · 23 min read #rag#llm#fine-tuning#long-context#ai

• Graph RAG, Explained: What It Is and When It Earns Its Cost

  The standard RAG recipe — chunk, embed, retrieve top-k — works when the answer sits inside a single chunk, but it stalls structurally on multi-hop questions and on global sensemaking questions that span the whole corpus

  2026-07-15 · 8 min read #rag#graph-rag#knowledge-graph#ai#llm

• Production RAG Patterns — Why Naive RAG Fails and the Techniques That Actually Help

  A demo RAG system takes half a day to build, but the place it quietly breaks in production is almost always retrieval, not generation. This reference walks through chunking, embeddings and hybrid search (BM25 + vector),

  2026-07-11 · 9 min read #ai#rag#llm#retrieval#embeddings

• How to Become an AI Engineer in 2026 — LLMs, RAG, Agents, Evals, and a Career Roadmap ♪ Listenable

  The complete 2026 guide to becoming an AI Engineer. From how the role differs from ML Engineer and Data Scientist to LLM APIs, prompt engineering, RAG design, agents, LoRA fine-tuning, vLLM serving, evals-driven developm

  2026-07-02 · 26 min read #ai-engineer#career#llm#rag#ai-agents

• SOTA Text Embedding Models — The Heart of Search and RAG

  Text embeddings are the heart of search and RAG systems. We walk through contrastive learning and InfoNCE, dual encoders, hard negatives, the E5/BGE/GTE families, Matryoshka representation learning, and the MTEB benchmar

  2026-06-30 · 14 min read #ai-papers#embedding#retrieval#rag#contrastive-learning

• Whose Words Does the AI Speak — The German Ruling That Made Google Liable for AI Overviews ♪ Listenable

  In June 2026 a German court classified Google AI Overviews not as a listing of search results but as speech by Google itself, holding the company directly liable for false answers. We dissect the reasoning, the clash wit

  2026-06-12 · 22 min read #ai#law#liability#rag#governance

• Context Engineering — How to Design Memory for AI Agents

  The era of prompt engineering is fading, and context engineering has emerged as the core skill. This post covers the economics of the context window, memory tier design, patterns for automatically extracting facts from c

  2026-06-12 · 17 min read #ai#context-engineering#llm#ai-agent#memory

• Vector Databases 2026 Complete Guide - Pinecone, Weaviate, Milvus, Qdrant, Chroma, LanceDB, pgvector, Vespa, Turbopuffer Deep Dive ♪ Listenable

  The 2026 vector database market is a Cambrian explosion. Pinecone serverless, Weaviate 1.27, Milvus 2.5, Qdrant 1.13, Chroma 0.5, LanceDB 0.20, pgvector 0.8, Vespa, Turbopuffer — over ten camps competing with their own o

  2026-05-16 · 28 min read #english#vector-database#pinecone#weaviate#milvus

• Vector Databases 2026 Deep-Dive — Pinecone, Weaviate, Qdrant, Milvus, pgvector, LanceDB, Chroma, FAISS, DiskANN

  A May 2026 deep-dive into the vector DB ecosystem that powers RAG and semantic search. Covers Pinecone Serverless v3, Weaviate, Qdrant (Rust), Milvus 2.5 + Zilliz, Chroma, LanceDB, pgvector + pgvectorscale + ParadeDB, Ve

  2026-05-16 · 18 min read #vector-database#pinecone#weaviate#qdrant#milvus

• No-Code AI Builders 2026 Deep Dive - Flowise · Langflow · Dify · Coze · n8n AI · Rivet · Vectara · Promptflow · LlamaIndex ♪ Listenable

  A May 2026 map of the no-code and low-code AI builder ecosystem. Covers graph-style LLM builders (Flowise, Langflow, Dify, Coze), enterprise and managed RAG options (Microsoft Promptflow, Vectara, LlamaIndex Cloud, Stack

  2026-05-16 · 31 min read #english#no-code-ai#flowise#langflow#dify

• Top LLM Papers 2024-2026 - Llama, DeepSeek, Qwen, Mistral, Phi, RLHF, DPO, CoT, RAG, FlashAttention, vLLM Reading List ♪ Listenable

  A curated reading list of 30+ must-read LLM papers for engineers building with LLMs in 2024-2026. Covers foundation models (Llama 3/4, DeepSeek-V3/R1, Qwen3, Mistral, Phi-4, Gemma 3), training innovations (MoE, MLA, GQA)

  2026-05-16 · 19 min read #llm#papers#llama#deepseek#qwen

• Graph Databases & Knowledge Graphs 2026 Deep Dive — Neo4j 5 · ArangoDB · Memgraph · TigerGraph · Amazon Neptune · Apache AGE · Kuzu · FalkorDB · Dgraph · GraphRAG ♪ Listenable

  A complete 2026 survey of graph databases and knowledge graphs. Covers Neo4j 5, ArangoDB, Memgraph, TigerGraph, Amazon Neptune, Apache AGE, Kuzu, FalkorDB, Dgraph and other graph DBs, GraphDB/Stardog/Virtuoso/Apache Jena

  2026-05-16 · 24 min read #graph-database#knowledge-graph#neo4j#arangodb#memgraph

• Local AI & On-Device LLMs 2026 — Ollama · LM Studio · Jan · Msty · Open WebUI · GPT4All · AnythingLLM · Faraday Deep Dive ♪ Listenable

  By May 2026, local AI is no longer a hobby. An M4 Max MacBook Pro runs Llama 4 Scout 109B MoE at 24 tokens per second. Desktop runtimes like Ollama, LM Studio, Jan, and Msty unify GUI and CLI, while Open WebUI, AnythingL

  2026-05-16 · 23 min read #local-ai#on-device-llm#ollama#lm-studio#jan

• Enterprise AI Search & Knowledge Platforms 2026 — Glean, Guru, Coveo, Atlassian Rovo, Notion Atlas, Microsoft 365 Copilot, Slack AI Deep Dive ♪ Listenable

  A complete map of the 2026 enterprise AI search market. With knowledge scattered across Slack, Confluence, Drive, Notion, Jira, and Salesforce, how does RAG over corporate data change employee productivity? Glean (raised

  2026-05-16 · 21 min read #enterprise-search#glean#guru#coveo#atlassian-rovo