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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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I built a token-level debugger for comparing two LLMs

I built a token-level debugger for comparing two LLMs

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1 min read
I Built a Vector Database Project from Scratch — Here’s What Actually Happened

I Built a Vector Database Project from Scratch — Here’s What Actually Happened

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3 min read
Context Windows Are Getting Enormous — Here Is What That Actually Changes

Context Windows Are Getting Enormous — Here Is What That Actually Changes

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2 min read
NyayAI: Building an AI Legal Assistant for 1.4 Billion People — A Technical Deep Dive

NyayAI: Building an AI Legal Assistant for 1.4 Billion People — A Technical Deep Dive

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21 min read
Google Just Made Gemma 4 Feel Like a Beta Test. Here's the Real Upgrade.

Gemma 4 Challenge: Write about Gemma 4 Submission

Google Just Made Gemma 4 Feel Like a Beta Test. Here's the Real Upgrade.

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3 min read
FLAMEHAVEN FileSearch: Why This RAG Engine Feels Different from the Usual Stack

FLAMEHAVEN FileSearch: Why This RAG Engine Feels Different from the Usual Stack

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9 min read
Scrape vs Crawl vs Map: Picking the Right Anakin API for the Job

Scrape vs Crawl vs Map: Picking the Right Anakin API for the Job

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4 min read
Revisiting My Phone AI After Gemma 4: The Upgrade I Didn't Know I Needed

Revisiting My Phone AI After Gemma 4: The Upgrade I Didn't Know I Needed

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4 min read
NyayAI: AI-Powered Legal Intelligence for India

NyayAI: AI-Powered Legal Intelligence for India

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13 min read
How AI Memory Actually Works: Context Windows and RAG

How AI Memory Actually Works: Context Windows and RAG

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8 min read
Semantic Chunking with Overlap and Section-Awareness: The RAG Tutorial Nobody Wrote

Semantic Chunking with Overlap and Section-Awareness: The RAG Tutorial Nobody Wrote

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8 min read
Stop Benchmarking Embedding Models. 90% of Your Search Quality Lives Upstream.

Stop Benchmarking Embedding Models. 90% of Your Search Quality Lives Upstream.

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4 min read
Applied Claude: Data Recovery, Agent Orchestration, Real-time Content

Applied Claude: Data Recovery, Agent Orchestration, Real-time Content

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3 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
The Hidden Compliance Gap in Every Enterprise RAG Pipeline

The Hidden Compliance Gap in Every Enterprise RAG Pipeline

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5 min read
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