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Handle search exceptions in talent discovery

Talent discovery uses AI to search across employee data and return the most relevant matches. Sometimes, results may look unexpected. This guide explains why and how to handle those cases.

Common edge cases

  • Broad or vague queries may return no results because the system requires specific search input to identify matching employees.
    Example: A query such as “employees with all skills” may not return matches because it is too general. To improve results, use specific keywords such as skills, roles, departments, business units, or locations.
  • Narrow queries may return few or no results when the system cannot find employees who match all criteria exactly.
    Example: Searching for “Senior Java Developer in Bangalore with 10+ years of experience and PMP certification” may return limited results if very few employees meet every requirement. In such cases, remove one or two filters or broaden the keywords.
  • Search results may include related roles even if the exact term is not used because AI captures the intent behind the search query and identifies similar matches based on contextual meaning, not only exact keywords.
    Example: Searching for “Software Engineer” may return employees with the title “Software Developer” if the AI determines the roles are contextually related. Review the results and refine the query further if needed to narrow the matches.
  • Results may appear in an unexpected order because AI ranks employees based on relevance across multiple data points.
    Example: An employee with strong matching skills and certifications may appear above another employee with the same job title but fewer relevant qualifications. Review ranking logic and adjust filters to prioritize the most important criteria.

Best practices

Use clear, specific keywords and apply filters for department, location, or business unit. Combine criteria thoughtfully, such as role plus skill plus location. Regularly review and update employee data. Understand that AI ranking considers multiple factors beyond job title.