Tag

#rag

10 posts

AI & Machine LearningJuly 25, 202616 min
Pinecone Nexus: Is RAG Ending for AI Agents? (2026)
Pinecone Nexus, a knowledge engine for AI agents, compiles enterprise data upfront to cut the RAG retrieval loop. What changes in 2026 — and if RAG is dead.
LLM & IntegrationJune 28, 202616 min
Token-Aware Text Chunking for RAG in TypeScript (2026)
Token-aware text chunking for RAG in TypeScript: recursive splitting with @langchain/textsplitters and js-tiktoken, plus chunk size and overlap tuning.
Security & PrivacyJune 21, 202610 min
AI Search Poisoning: 13 Words That Rig AI Answers (2026)
Cornell Tech's WARP attack shows ~13 words in a Reddit comment can poison AI deep-research agents into naming fake products in 38–62% of exposed runs.
AI & Machine LearningMay 16, 202622 min
pgvector HNSW Postgres 18 Production Tuning Tutorial 2026
Tune pgvector 0.8.2 HNSW indexes on PostgreSQL 18: ef_search, halfvec quantization, iterative scans, and parallel builds with a runnable Docker setup.
AI & Machine LearningMarch 5, 202632 min
Local AI with Ollama + Qwen3: RAG, Agents & Vector Stores
Production local AI on your own hardware: Ollama + Qwen3, ChromaDB RAG, tool-calling agents, quantization, and security. Runnable code, zero cloud.
LLM & IntegrationMarch 2, 202611 min
LM Studio 2026: Run Local LLMs With GPU Acceleration
LM Studio runs open-source LLMs locally on Windows, Mac (Apple Silicon), and Linux. Setup, GPU (CUDA/Metal/Vulkan/ROCm), model picks, and RAG in one guide.
LLM & IntegrationFebruary 21, 20269 min
Building a Robust RAG System: A Complete Implementation Guide
Build a robust RAG system end to end: chunking, embeddings, vector stores, hybrid retrieval, reranking, and eval harnesses you actually need in production.
LLM & IntegrationDecember 13, 202511 min
RAG Optimization: Smarter Retrieval Systems
RAG optimization: chunk sizing, hybrid retrieval, reranking, query rewriting, and evaluation — smarter retrieval-augmented systems that actually rank well.
LLM & IntegrationDecember 4, 202511 min
The Future of LLMs and Fine‑Tuning: From Foundation Models to Custom Intelligence
The future of LLMs and fine-tuning: LoRA, adapters, RAG, synthetic data, and the modular techniques replacing full retraining in 2026 production workflows.
LLM & IntegrationNovember 18, 202511 min
How to Solve Common RAG Failures
Fix common RAG failures: bad chunking, irrelevant embeddings, outdated data, and ambiguous queries. Diagnostic steps, retrieval evals, and patches that work.

Every post in #rag (10)