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# rag

Retrieval augmented generation, or RAG, is an architectural approach that can improve the efficacy of large language model (LLM) applications by leveraging custom data.

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SQLite FTS5 won't tokenize Chinese — here's the 7-line bigram fix that did

SQLite FTS5 won't tokenize Chinese — here's the 7-line bigram fix that did

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4 min read
Chunking Strategies for LLM Applications: A Practical Guide to Better RAG Systems

Chunking Strategies for LLM Applications: A Practical Guide to Better RAG Systems

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4 min read
Local LLMs on Mobile, Enterprise Code Gen Workflows, & Production AI Cost Management

Local LLMs on Mobile, Enterprise Code Gen Workflows, & Production AI Cost Management

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3 min read
Beyond Keywords: Mastering HyDE for Smarter Retrieval đź§ 

Beyond Keywords: Mastering HyDE for Smarter Retrieval đź§ 

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4 min read
Part 2: RAG Architecture: How Retrieval-Augmented Generation Actually Works

Part 2: RAG Architecture: How Retrieval-Augmented Generation Actually Works

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4 min read
Part 3: Types of RAG

Part 3: Types of RAG

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3 min read
Why I picked Ollama + LanceDB + FastAPI for the AI Book Recommender

Why I picked Ollama + LanceDB + FastAPI for the AI Book Recommender

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3 min read
Knowledge Base Software for B2B Support: Architecture, API Design, and AI Readiness

Knowledge Base Software for B2B Support: Architecture, API Design, and AI Readiness

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13 min read
The Gate: Architecting Deterministic Grounding for the Agentic Era

The Gate: Architecting Deterministic Grounding for the Agentic Era

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6 min read
LLM Engineering: Architecting Agentic RAG and Conversational BI

LLM Engineering: Architecting Agentic RAG and Conversational BI

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3 min read
Why Companies Will Stop Asking “Do You Know AI?” and Start Asking This Instead

Why Companies Will Stop Asking “Do You Know AI?” and Start Asking This Instead

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

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

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

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

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4 min read
I kept losing project context, so I built a local Mac workspace with MCP, CLI, and hybrid RAG

I kept losing project context, so I built a local Mac workspace with MCP, CLI, and hybrid RAG

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

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

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1 min read
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