Glossary

Windowing

What is Windowing?

Windowing means splitting long data into sections (“windows”) of fixed length, which often overlap. In AI applications this divides long documents into searchable parts; the overlap ensures that a sentence at a section boundary does not lose its context. Sliding windows are also used with time series and audio signals to analyse data piece by piece.

How KI.ordo helps

KI.ordo splits stored knowledge into overlapping sections so that knowledge search finds relevant passages together with their context.

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Topics

Knowledge search
Documents
RAG

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Related terms

Retrieval-augmented generation (RAG)

Retrieval-augmented generation combines search with text generation: before answering, the system searches a knowledge collection – such as manuals, policies or FAQs – for relevant passages and passes them to the language model as context. The answer is therefore based on current, company-specific content rather than only on the model's training knowledge. This reduces invented statements, makes sources traceable and allows updates without retraining.

RAG
knowledge search
+1

Information Retrieval

Information retrieval covers methods that find the documents or passages in a large body of text that match a query and rank them by relevance. Classic approaches such as BM25 weight search terms by how often and how rarely they occur; newer ones add semantic similarity. It underpins search engines, enterprise search and retrieval-augmented generation, in which a language model uses the retrieved passages as the basis for its answer.

Search
BM25
+1

Retrieval pipeline

A retrieval pipeline is the sequence of processing steps a system uses to find the right information for a question. Typical steps are: ingesting and cleaning documents, splitting them into chunks, building a search index, optionally rewriting the query, retrieving matches, ranking them (reranking) and passing the best ones to the language model. It is the foundation of every RAG application, and its quality largely determines how good the resulting answers are.