At a glance<\/strong><\/p>\n\n Artificial Intelligence<\/strong> is the broad term for software that performs tasks such as interpreting language, recognizing patterns, or generating text. In recruiting, those tasks might include organizing application details, suggesting search terms, or drafting outreach. The label alone tells you little about how a feature works.<\/p>\n\n Machine Learning<\/strong> is an approach in which a system learns patterns from examples rather than relying only on rules written by a person. A model might learn that several job titles describe similar responsibilities. That does not mean it understands every specialty or knows whether an individual is qualified.<\/p>\n\n A model<\/strong> is the trained component that produces an output from an input. Inference<\/strong> means using that model: you submit a profile or question, and it returns a classification, prediction, or response.<\/p>\n\n Generative AI<\/strong> produces new content, such as a candidate summary or interview question. An AI agent<\/strong> combines model outputs with tools to carry out steps, such as searching records and preparing a draft message. Its permitted actions matter as much as its language abilities.<\/p>\n\n For your engineer search, distinguish finding equipment references from writing a convincing summary about them. Ask what each feature receives, what it produces, and what it can change. The useful definition describes a task, not just a label.<\/strong><\/p>\n\n Natural Language Processing<\/strong> covers methods for working with human language. Recruiting applications include identifying skills in a resume, interpreting a search request, and extracting availability from written answers. These tasks require attention to context: \u201csupported installation\u201d is not necessarily \u201cled installation.\u201d<\/p>\n\n A large language model<\/strong> generates language based on patterns learned during training and the context supplied to it. A prompt<\/strong> is the instruction or question you provide. Asking for \u201ca strong candidate summary\u201d invites a different response from asking for \u201cdocumented equipment experience, with unsupported claims marked unknown.\u201d<\/p>\n\n Hallucination<\/strong> means generated content that is unsupported or incorrect. If an applicant mentions general maintenance, a summary must not quietly upgrade that statement to expertise with your required equipment. Fluent wording is not proof.<\/p>\n\n Retrieval-augmented generation<\/strong> brings retrieved material into the generation process. For example, a system might consult application answers before drafting a summary. This can make answers more grounded, but the retrieved material may be incomplete or outdated.<\/p>\n\n Request a source reference for important claims and check whether that source actually supports the wording. A citation attached to a paragraph is not enough if the paragraph adds qualifications the source never states. Evaluate the claim and its evidence together.<\/strong><\/p>\n\n An Applicant Tracking System<\/strong> stores applications and manages stages such as review, interview, and offer. It is not automatically an AI system. For Talent Acquisition teams, it often serves as the working record of who applied, what happened, and what needs attention.<\/p>\n\n Resume Parsing<\/strong> turns resume content into structured fields, including employers, dates, titles, and skills. This is extraction, not verification. A parser can place an equipment name in the right field without establishing whether the applicant used it independently.<\/p>\n\n A schema<\/strong> defines how information is organized. Your engineer search might need separate fields for equipment family, diagnostic experience, travel availability, and the date each detail was confirmed. Combining all four into one notes field makes later filtering harder.<\/p>\n\n An API<\/strong> is a defined way for systems to exchange information. Integration<\/strong> is the wider connection between tools, including field mapping, permissions, and update behavior. Ask whether a change replaces an existing value, adds a note, or creates a duplicate.<\/p>\n\n When you assess Twenty One Nexus or another internal workflow, inspect a record before and after an update. Can you distinguish the applicant\u2019s original answer from an extracted value and a recruiter\u2019s correction? Reliable automation depends on records that preserve meaning and origin.<\/strong><\/p>\n\n Boolean search<\/strong> combines explicit terms with operators such as AND, OR, and NOT. It is useful when you know the wording you need. Its weakness is that a qualified person may describe the same experience differently.<\/p>\n\n Semantic search<\/strong> looks for similarity in meaning rather than exact wording alone. An embedding<\/strong> is a numerical representation that helps a system compare text. Similarity can surface related profiles, but related experience is not necessarily interchangeable experience.<\/p>\n\nAI Terms Behind Recruiting Features<\/h2>\n\n
Language Tools And Generated Answers<\/h2>\n\n
Records, Parsing, And System Connections<\/h2>\n\n
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<\/figure>\n\nSearch And Candidate Matching Terms<\/h2>\n\n