Cross-Encoders vs Bi-Encoders
The architecture backbone of modern AI search engines, RAG pipelines, and recommendation systems
Maximal Marginal Relevance: The Redundancy Problem RAG Pipelines Don't Talk About
Better embeddings. Better chunking. Better reranker. Almost nobody optimizes for the fact that "top 5 most similar" can mean "the same sentence, five times."
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Coreference Resolution: The Preprocessing Step Most LLM Pipelines Skip
It's not a RAG problem. It's a "does your system know what 'it' means" problem - and it shows up everywhere language meets action.
Read ArticleHyDE and IRCoT: Retrieval Tricks That Actually Improve Domain-Specific RAG
Most of the tutorials stop at the “embed the query, use cosine similarity” but the magic of RAG is in something else…
Read ArticleRAG vs Fine-Tuning: A Builder's Framework for Choosing the Right AI Strategy
Let us be honest. Right now, the sheer number of options surrounding us as developers is overwhelming. Every single day, a new architecture, a new buzzword, or a new framework drops — and we are left staring at our screens wondering which strategy to opt for.
Read ArticleBeyond the Browser: Investigating the Domain Name System
A deep dive into what happens in the 100ms after you type a URL. Uncovering the invisible global relay race of the Domain Name System.
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