Purpose of This Document This document records the historical context, architectural motivations, and rationale behind the design decisions of FACET. It exists...

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🔍 What Is an LLM, Really? At its core, an LLM is a next‑token prediction system. Given a sequence of tokens words or word pieces, the model predicts the most...

When indexing hurts more than it helps: how we realized our RAG use case needed a key-value store, not a vector database The post When Not to Use Vector DB appe...

It has become increasingly clear in 2025 that retrieval augmented generation RAG isn't enough to meet the growing data requirements for agentic AI. RAG emerged...

Originally published on Principia Agentica The OptiPFair Series – Episode 1 A deep‑dive exploration of Small Language Models SLM optimization. The AI race has...

You ever feel like you’re using just 10% of ChatGPT’s brainpower? I did — until I fell headfirst into the rabbit hole of secret prompts and hidden behaviors. Tu...

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TL;DR I built a benchmark suite to test various optimizations for streaming LLM responses in a React UI. Key takeaways: 1. Build a proper state first, then opt...

After multiple iterations, experiments, and lessons learned, I finally built a 550 M‑parameter model completely from scratch. This isn’t my first time building...

What worked, what broke, and why I did it The post Lessons Learned from Upgrading to LangChain 1.0 in Production appeared first on Towards Data Science....

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