Chunking Strategies and Boundary Effects Chunking is where retrieval becomes physical. A system takes the continuous experience of reading and turns it into discrete units that can be embedded, indexed, and returned under latency constraints. The chunking strategy sets the ceiling for answer quality because it determines what evidence the model can see and cite. […]

Cross-Lingual Retrieval and Multilingual Corpora Global AI systems live in a multilingual reality. Users ask questions in one language and expect relevant evidence that may exist in another. Enterprises store policies in English, support tickets in Spanish, engineering notes in Japanese, and product documentation in a mix of languages and dialects. The infrastructure challenge is […]

Deduplication and Near-Duplicate Handling Retrieval systems do not only retrieve knowledge. They retrieve whatever you put into them, including repeated copies of the same page, syndication mirrors, boilerplate variants, and rewritten duplicates that differ only by a banner or a date stamp. If duplicates are allowed to accumulate, they quietly sabotage quality and cost in […]

Embedding Selection and Retrieval Quality Tradeoffs Embeddings are the interface between language and infrastructure. They translate text into vectors, and that translation defines what “similarity” means for the entire retrieval system. Everything downstream inherits the strengths and blind spots of that embedding choice: indexing behavior, latency, costs, and the model’s ability to stay grounded in […]

Index Design: Vector, Hybrid, Keyword, Metadata Retrieval systems feel magical when they work and brittle when they do not. The difference is rarely “better AI” in the abstract. It is usually index design: how content is represented, stored, filtered, and searched so that a query can produce strong candidates fast enough to be useful. The […]