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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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Is It Still RAG If There's No Retrieval?

Is It Still RAG If There's No Retrieval?

Comments 5
8 min read
What 113 eval questions taught me building a fully local RAG

What 113 eval questions taught me building a fully local RAG

Comments 1
9 min read
Retrieval Latency Budgets in RAG Pipelines: Vector Search, Reranking, and the Timeout Cascade Problem

Retrieval Latency Budgets in RAG Pipelines: Vector Search, Reranking, and the Timeout Cascade Problem

Comments
6 min read
Day 14: RAG Evaluation and Hallucination Guardrails

Day 14: RAG Evaluation and Hallucination Guardrails

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3 min read
Evaluating RAG — retrieval + generation metrics that matter

Evaluating RAG — retrieval + generation metrics that matter

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Comments 2
8 min read
How I Built a RAG Search Engine for 10,000 Documents — and Why I Eventually Dropped Answer Generation

How I Built a RAG Search Engine for 10,000 Documents — and Why I Eventually Dropped Answer Generation

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10 min read
RAG evaluation metrics explained

RAG evaluation metrics explained

Comments 1
5 min read
Day 07/90:Taking RAG from Notebook to Production — Challenges and Research-Backed Solutions

Day 07/90:Taking RAG from Notebook to Production — Challenges and Research-Backed Solutions

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3 min read
Scope chat agents with citations and multi-pass search

Scope chat agents with citations and multi-pass search

Comments 3
5 min read
How to Convert PDFs to Markdown for RAG and Chatbots (and When Not To)

How to Convert PDFs to Markdown for RAG and Chatbots (and When Not To)

Comments 2
7 min read
How I Debugged a KV-Cache Offloading Bug in vLLM

How I Debugged a KV-Cache Offloading Bug in vLLM

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Comments 2
2 min read
Day 13: Retrieval-Augmented Generation (RAG) — Chatting with Proprietary Documents

Day 13: Retrieval-Augmented Generation (RAG) — Chatting with Proprietary Documents

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3 min read
A Field Guide to RAG Failure Modes: Garbled Scans, Duplicate Chunks, and Leaky Indexes

A Field Guide to RAG Failure Modes: Garbled Scans, Duplicate Chunks, and Leaky Indexes

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Comments 2
6 min read
Chunking: Getting the First Cut Right

Chunking: Getting the First Cut Right

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7 min read
Two Identical Chunks Are Not Always Duplicates

Two Identical Chunks Are Not Always Duplicates

Comments 3
26 min read
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