Architecture

RAGAS and DeepEval both score RAG systems: faithfulness, context precision, contextual relevancy. The metrics overlap 80%. What separates them is where and how you use them. RAGAS was born from a research paper. DeepEval was born from a CI/CD need. It shows in both APIs, and it determines which one you should install first. On…

You want to fine-tune an LLM. Chances are, you don’t need to. There’s a trap that almost every developer falls into the first time they explore fine-tuning. They see an LLM that doesn’t know their product, their terminology, or their brand voice. So they start looking for ways to adapt it. They come across fine-tuning….

I used to think vector search was enough.

It isn’t.

Embeddings capture the overall meaning of a text, not its fine-grained requirements.

And when you search for ** »React Senior Paris »** and get ** »React Junior Lyon »** as the top result, that’s exactly the problem: a search engine that understands the domain, but not the actual query.

I wrote a deep dive on the **Retrieve → Re-rank** pipeline: why Dense Retrieval alone eventually hits its limits, how Cross-Encoders fix the problem, and how the two work together in modern RAG architectures.