Information Retrieval

A retrieval system represents a collection and a query so it can identify matching candidates efficiently. Lexical approaches use inverted indexes and term-based scores; dense approaches compare learned vectors; hybrid methods combine signals. Filters restrict eligible material, and reranking can apply a more expensive relevance model to a candidate set. Document segmentation and field weighting affect what a result means. Relevance is defined by the user's need, which may involve topical match, exact identifiers, authority or recency. Precision, recall and ranking metrics examine different aspects of success. Competence includes building a judgment set and understanding how query construction, index coverage and ranking each contribute to the final result.

Specify the collection, permissions and searchable unit, then inspect content extraction and metadata. Construct realistic queries with judged relevant results and keep tuning queries separate from final evaluation. Establish a lexical baseline before testing vector or hybrid methods. Measure candidate recall and final rank quality separately, including empty or ambiguous queries. Inspect missing documents, incorrect filters and ranking errors as different failure sources. Version the corpus and index and test updates. The deliverable is a search configuration whose relevance and coverage are supported by evidence, with clear handling for absent material and results outside the user's permitted scope.

Relevance judgments are incomplete and depend on the information need, so one benchmark may not represent another application. A ranker cannot recover documents missing from the index or removed by an incorrect filter. High recall can coexist with poor first results, and semantically similar text may lack the required fact. Query repetition during tuning can overfit evaluation. Distinguish retrieval, summarization and question answering, and measure index coverage, candidate selection and final ranking separately.