AI-assisted practical guide. Examples are hypothetical; these are proposed editorial methods, not reported research results.
When you have a large collection of files, similar names often mask very different contents. Automated deduplication tools that rely solely on name similarity can mistakenly delete files that are merely related versions. By using a large‑language model (LLM) as a linguistic assistant, you can separate naming patterns from actual duplicates—provided you supply the filenames yourself and perform the final checks manually.
Analyzing Naming Conventions
Start by extracting the list of filenames from your storage system and placing that list into the prompt you give the LLM. In the prompt, ask the model to look for common elements such as dates, version identifiers, project codes, or descriptive suffixes. Request that it group the names according to these markers rather than by simple alphabetical order. For cases where naming is inconsistent, you can ask the model to highlight recurring keywords or patterns that appear in different positions within the strings. The LLM will return a textual classification that shows which files appear to belong to the same workflow, which are likely earlier drafts, and which seem unrelated. Remember that the model works only with the text you provide; it does not read the file system or inspect the files themselves.
Hypothetical example
Imagine the filename list contains Project_Alpha_Draft_V1.docx, Project_Alpha_Final_v1.docx and Project_Alpha_Final_v2.docx. Ask the model to group names by their visible shared text and report apparent version labels without inferring contents. A suitable answer groups all three under Project Alpha and records Draft V1, Final v1 and Final v2 as filename labels. It states that chronology, actual approval and duplicate content remain unverified. A human can compare file metadata and contents separately. The word Final in a name does not establish a released document.
Verifying the Analysis
The LLM’s categorization is only a suggestion. You must compare its groups with the actual files in your directory. Check the timestamps, file sizes, and, if needed, open the documents to confirm whether they are distinct versions or true duplicates. If the model groups two files as separate versions but you discover they have identical sizes and timestamps that are only seconds apart, that likely indicates an accidental duplicate that should be addressed. Conversely, if two files share a name pattern but differ substantially in size or creation date, they are probably different assets despite the naming similarity. By performing this manual verification, you can check whether no genuine duplicates are removed and that versioned files remain organized correctly.