Posts
All the articles I've posted.
Reranking and Late Interaction: Cross-Encoders to ColBERT
Updated:Retrieval has to be cheap per document because it scores every document; reranking can be expensive per document because it only sees the top few. The bi-encoder and cross-encoder, their cost per query computed, ColBERT's late interaction and MaxSim worked token by token, why max and not mean, hard negatives, index sizes with ColBERTv2's compression, and LLM rerankers. Then cross-encoder reranking measured on the English and Vietnamese benchmark of Semantic and Hybrid Search, with what it gains and what it costs.
Engineering Around the Model: Harnesses, Loops, Graphs and the Definition of Done
Updated:The model sets the ceiling; the harness around it decides how close an agent gets. The parts of a harness; loop design (stopping conditions, retries, budgets); graphs and when they beat a loop; and the definition of done as a check the harness runs instead of a claim the model makes. Measured on a small agent: a verifier that checks only where the answer came from, against the same agent without it, with success at each step budget; and what Anthropic's long-running harnesses do about agents that declare victory early.
Beyond Vector RAG: Agentic, Graph and Vectorless Retrieval
Updated:When one retrieval pass can't answer the question, there are three ways to go further: let the model decide what to fetch next (agentic RAG), fetch structure instead of chunks (GraphRAG), or navigate a document's own outline instead of embedding it (vectorless retrieval). How each works and what it costs, with GraphRAG's indexing bill computed for this blog, and keyword, dense, hybrid and LLM-navigated retrieval compared on 30 questions about it.
AI Engineering, from the Inside: The Map
Updated:Where this series starts, where it ends, and how the pieces connect: six words that name the layers of every AI system, a map of the seventeen parts, one running example that grows through every post, the notation used throughout, and a first cost estimate for a single answer.