embeddings

ai model exit strategy a stacked hexagonal plates

AI Model Exit Strategy: Proven Guide to Avoid Costly Risk

Model portability is an engineering property, not a legal one. This guide sets out where AI lock-in actually forms across five layers — the API surface, the prompt estate, embeddings, fine-tunes and the evaluation record — and what a switch really costs at each level of preparation. It covers the abstraction patterns that pay for themselves, why an evaluation harness is the instrument that makes a switch defensible, which assets transfer between models and which do not, the contract terms that keep the door open, and a 90-day runbook you can run without pausing delivery.

Read more
rag chunking and document parsing a featured block sliced into slabs

RAG Chunking and Parsing: Proven Fixes for Better Answers

Retrieval systems are judged on their answers but built on their chunks. This guide covers the two steps that create them: parsing, which turns a file into usable text, and chunking, which cuts that text into the units your index stores. It compares fixed, recursive, structure-aware and semantic splitting, sets out workable size and overlap ranges, explains the metadata that turns a search result into a citable answer, handles the document types that defeat generic splitting, and shows how to measure retrieval with a gold question set before you trust it.

Read more
CHAT