AI-assisted practical guide. Examples are hypothetical; these are proposed editorial methods, not reported research results.
When a client or manager requests a document using vague language, the primary challenge is translating subjective descriptions into precise search terms. AI can bridge this gap by analyzing the context of the request to suggest a variety of keywords and boolean operators. The goal is to identify the likely terminology used in the filing system without assuming the specific document is currently available or correctly named.
Extracting Searchable Keywords
To begin, feed the vague request into the AI and ask it to identify potential synonyms, industry jargon, and related technical terms. Instead of asking the AI to find the document, ask it to generate a list of terms a professional in that field would use to label such a file. If the request is particularly ambiguous, suggest that the AI provide three different search strategies: one broad, one narrow, and one based on common naming conventions. This can help prevent the searcher from becoming tunnel-visioned on a single phrase that might not match the actual file name.
Hypothetical example
Imagine a request says, “Find the report about power issues in the east wing from a few years ago.” Ask the model for candidate search terms and unresolved details. It can propose electrical, power, outage and “east wing,” while asking which building and approximate years are intended. If the chosen search tool supports Boolean syntax, a proposed query is (electrical OR power OR outage) AND “east wing.” Otherwise try the terms separately using that tool's supported search controls. The human checks returned titles and document contents; a polished query does not establish that a matching report exists.
Validating the Query Results
Once you have the suggested queries, run them against your archive and evaluate the results. The difficult case occurs when a query returns thousands of irrelevant hits. In this instance, refine the AI prompt by providing a few examples of documents that are similar but incorrect, asking the AI to identify the differentiating keywords. To check the finished result, review the top five documents retrieved to ensure they align with the original intent of the vague request. If the results are still too broad, add a date range or a specific author to the query.
To implement this, use the following prompt: "Convert this vague document request into three distinct search queries using boolean logic to maximize retrieval probability: [Insert Request]." For example, if the input is "the thing about the merger rules," the output might be "merger AND regulations" or "M&A AND compliance." A human check should verify that the AI has not hallucinated a specific document title that does not exist in the actual database.