Every RAG pipeline starts with the same piece of code: split documents into chunks, embed them, store them. It looks
Continue readingRAG Chunking Is Not Settled: What Token-Level Evaluations Actually Show
Most teams that ship a retrieval-augmented generation feature never measure it. The demo looks good, a few hand-picked questions return
Continue readingStop Vibing Your RAG Pipeline: Recall@k, MRR, and NDCG in an Afternoon
Every RAG system has two halves: a retriever that decides which text the model sees, and a generator that answers
Continue readingRAG Chunking Strategies in 2026: What the Benchmarks Actually Show
Shipping an LLM-powered feature without an evaluation pipeline is like deploying a microservice without health checks — it might work
Continue readingBuilding LLM Evaluation Pipelines: Metrics, Frameworks, and CI Integration
The first version of any RAG pipeline usually looks the same: embed a query, search a vector store, stuff the
Continue readingBeyond Naive RAG: 4 Advanced Patterns That Actually Work in Production
Every week, the GitHub community surfaces projects that reshape how developers think about building software. This week is no different
Continue reading5 Trending GitHub Repos You Should Know About — May 2026
Entering the AI space feels like learning a new language. Everyone throws around RAG, RLHF, GGUF, MoE, MCP like you’re
Continue readingThe AI Glossary: Every Term You Need to Know in 2026