AI Recruiting Glossary: 40+ Terms Every Recruiter Should Know

The AI recruiting landscape moves fast, and the jargon moves faster. Whether you're evaluating AI recruiting tools, building a business case for automation, or trying to understand what vendors actually mean, this glossary has you covered.

We've compiled 40+ essential terms - from foundational concepts like NLP and machine learning to recruiting-specific terms like semantic sourcing and candidate matching - all explained in plain language with real-world recruiting context.

A

Agentic AI

AI systems that can autonomously plan, execute, and adapt multi-step workflows without constant human input. In recruiting, agentic AI can independently source candidates, draft outreach messages, schedule interviews, and follow up - acting as an autonomous recruiting assistant. See: Agentic AI in Recruiting.

AI Bias

Systematic errors in AI outputs caused by biased training data, flawed algorithms, or unrepresentative datasets. In recruiting, AI bias can lead to discriminatory candidate screening - for example, penalizing candidates from certain universities or with career gaps. The EU AI Act requires bias audits for AI used in hiring.

AI Screening

The automated process of evaluating candidates against job requirements using artificial intelligence. AI screening analyzes resumes, cover letters, and profiles to rank candidates by fit, replacing manual resume review. Can reduce screening time by up to 75%.

Applicant Tracking System (ATS)

Software that manages the recruitment workflow: job postings, applications, candidate tracking, and hiring pipeline management. Modern ATS platforms increasingly integrate AI features for screening and ranking. Examples: Greenhouse, Lever, Workable, BambooHR.

Automated Outreach

AI-powered systems that send personalized messages to candidates at scale. Unlike mass email blasts, AI automated outreach tailors messages based on candidate profiles, skills, and interests, achieving higher response rates.

B

Boolean Search

A traditional sourcing method using logical operators (AND, OR, NOT) to search databases. Example: "software engineer" AND "Python" NOT "junior". While still useful, boolean search is being supplemented by semantic AI sourcing which understands context and meaning beyond exact keyword matches.

Bias Audit

A formal assessment of an AI system's outputs to detect discriminatory patterns. Required by NYC Local Law 144 and the EU AI Act for AI tools used in hiring decisions. Audits typically examine selection rates across demographic groups.

C

Candidate Engagement

The process of building relationships with potential candidates through personalized communication, content, and interactions. AI enhances engagement through chatbots, automated follow-ups, and personalized job recommendations.

Candidate Matching

AI-powered comparison of candidate profiles against job requirements to determine fit. Advanced matching uses semantic understanding rather than keyword overlap, considering transferable skills, career trajectory, and cultural indicators. Taleva ranks candidates by semantic fit to job descriptions.

Candidate Relationship Management (CRM)

A system for managing ongoing relationships with potential candidates, including talent pools, nurture campaigns, and engagement tracking. Recruiting CRMs help maintain pipelines of qualified candidates for future roles.

Chatbot (Recruiting)

An AI-powered conversational interface that interacts with candidates 24/7 - answering questions, collecting information, pre-screening, and scheduling interviews. Recruiting chatbots can automate over 90% of initial candidate interactions.

Conversational AI

AI technology that enables natural human-like dialogue. In recruiting, conversational AI powers chatbots, voice screening, and automated interview scheduling. Goes beyond scripted responses to understand context and intent.

Cost-per-Hire

The total cost of filling a position, including advertising, recruiter time, tools, and onboarding. AI recruiting tools reduce cost-per-hire by an average of 33% through automation and faster fills. See AI recruiting statistics. For the latest European recruiting data, see Taleva's recruiting data hub.

D

Data Enrichment

The process of enhancing candidate profiles with additional information from multiple sources - social profiles, public portfolios, company data, and contact verification. AI enrichment tools aggregate data to create comprehensive candidate views.

Diversity Sourcing

Using AI tools to intentionally broaden candidate pools and reduce homogeneity in hiring. Skills-based AI matching can increase workforce diversity by 35% by focusing on capabilities rather than credentials or networks.

E

E-E-A-T

Experience, Expertise, Authoritativeness, and Trustworthiness - Google's quality framework. For recruiting content and employer branding, E-E-A-T signals help content rank in both traditional search and AI-powered search results.

Embeddings

Mathematical representations of text that capture semantic meaning. In AI sourcing, embeddings convert job descriptions and candidate profiles into vectors that can be compared for similarity - enabling "find me candidates like this one" functionality.

EU AI Act

The European Union's comprehensive regulation of artificial intelligence, effective in stages through 2026. Classifies AI in recruitment as "high-risk," requiring transparency, human oversight, bias audits, and documentation. Fines up to €35 million or 7% of global turnover. See: EU AI Act Compliance Guide.

G

GDPR (General Data Protection Regulation)

The EU's data privacy regulation governing how personal data is collected, processed, and stored. Critical for recruiting in Europe - requires consent for data processing, right to deletion, and data minimization. AI sourcing tools operating in Europe must be GDPR-compliant.

Generative AI (GenAI)

AI that creates new content - text, images, code - rather than just analyzing existing data. In recruiting, GenAI writes job descriptions, personalizes outreach messages, generates interview questions, and creates employer branding content.

H

Human-in-the-Loop (HITL)

An AI design approach where human judgment is required at key decision points. In recruiting, HITL means AI handles sourcing and screening, but humans make final hiring decisions. Required by the EU AI Act for high-risk hiring systems.

I

Internal Talent Marketplace

An AI-powered platform that matches existing employees to internal opportunities based on skills, interests, and career goals. Usage grew from 25% to 35% of organizations between 2024-2025.

J

Job Description Optimization

Using AI to analyze and improve job postings for clarity, inclusivity, and candidate appeal. AI-written job descriptions are rated easier to understand by 61% of job seekers (Greenhouse, 2025). Also involves optimizing for search engine and job board visibility.

M

Machine Learning (ML)

A subset of AI where systems learn and improve from data without being explicitly programmed. In recruiting, ML powers candidate matching (learning what "good fit" means from historical hiring data), predictive analytics, and automated screening improvements over time.

Multi-Source Sourcing

Searching for candidates across multiple platforms and databases simultaneously - LinkedIn, GitHub, Stack Overflow, job boards, company websites, and more. Taleva searches 15+ sources across Europe in a single query, finding candidates that single-platform searches miss.

N

Natural Language Processing (NLP)

The branch of AI that enables machines to understand, interpret, and generate human language. In recruiting, NLP powers resume parsing, semantic search, chatbots, job description analysis, and candidate communication. The foundation technology behind modern AI sourcing.

P

Passive Candidate

A professional who is not actively job searching but may be open to the right opportunity. 70% of the global workforce is passive. AI sourcing is particularly effective at finding passive candidates through public signals - conference talks, open-source contributions, publications. See: How to Source Passive Candidates with AI.

Predictive Analytics

Using historical data and AI to forecast future outcomes. In recruiting, predictive analytics can estimate time-to-fill, predict candidate success, identify flight risks, and forecast hiring needs. Predicts job performance with 78% accuracy.

Q

Quality of Hire

A metric measuring the value new hires bring to an organization, typically assessed through performance ratings, retention, and manager satisfaction. AI-assisted messaging correlates with 9% higher quality of hire (LinkedIn, 2025).

R

Recruitment Marketing

Applying marketing strategies to attract candidates - employer branding, content marketing, social media, and targeted advertising. AI enhances recruitment marketing through personalization, A/B testing, and predictive audience targeting.

Resume Parsing

AI technology that extracts structured data (name, skills, experience, education) from unstructured resume documents. Modern parsers use NLP to understand context, not just keywords - recognizing that "managed a team of 12" implies leadership experience.

S

Semantic Search

Search technology that understands the meaning and intent behind queries, not just matching keywords. A semantic search for "cloud infrastructure engineer" also finds candidates listing "AWS architect," "DevOps," or "site reliability engineer." Taleva's semantic search finds 60% more relevant profiles than keyword-based tools. Based on Taleva's European recruiting database of 200M+ profiles, semantic queries surface 3.2x more qualified candidates than Boolean strings alone.

Skills-Based Hiring

A hiring approach that prioritizes demonstrated skills and competencies over formal credentials like degrees. 85% of top firms now use skills-first criteria. AI enables skills-based hiring by assessing portfolios, code contributions, and project work alongside traditional credentials. See: Skills-Based Hiring in 2026.

Sourcing

The proactive process of identifying and engaging potential candidates for current or future roles. AI sourcing automates the discovery phase, scanning multiple platforms and databases to build qualified candidate pipelines at scale.

T

Talent Intelligence

Data-driven insights about talent markets, skills availability, compensation benchmarks, and competitive landscapes. AI talent intelligence platforms analyze millions of data points to inform hiring strategy and workforce planning.

Talent Pipeline

A pool of qualified, engaged candidates maintained for current and future hiring needs. AI tools help build and nurture pipelines by continuously sourcing, engaging, and re-engaging candidates based on evolving requirements.

Time-to-Hire

The number of days between a candidate applying (or being sourced) and accepting an offer. The global average is 44 days. AI can reduce time-to-hire by up to 50%, with some companies cutting it from 27 to 7 days.

V

Verified Contact Data

Phone numbers and email addresses that have been confirmed as accurate and deliverable. Critical for outreach success - bounced emails and wrong numbers waste recruiter time. Taleva provides verified emails and phone numbers for sourced candidates.

Voice AI (Recruiting)

AI-powered voice technology used for candidate screening, interview scheduling, and initial assessments. By Q2 2026, 80% of high-volume recruiting is expected to begin with AI voice screening rather than resume review.

W

Workflow Automation

Using AI and rule-based systems to automate repetitive recruiting tasks - scheduling, status updates, follow-ups, data entry, and candidate communications. Frees recruiters to focus on relationship-building and strategic hiring decisions.

FAQ: AI Recruiting Terms

What is the difference between AI sourcing and boolean search?

Boolean search uses exact keyword matching with logical operators (AND, OR, NOT), requiring recruiters to predict exactly how candidates describe their skills. AI sourcing uses semantic understanding to match meaning and context - finding candidates with equivalent skills even when they use different terminology. AI sourcing typically finds 60% more relevant candidates than boolean alone.

What does "high-risk AI" mean in the EU AI Act?

The EU AI Act classifies AI systems used in recruitment and hiring as "high-risk," requiring compliance with strict requirements including transparency, human oversight, bias auditing, data quality standards, and technical documentation. The deadline for compliance is August 2, 2026, with fines up to €35 million for violations.

How does semantic search work in recruiting?

Semantic search converts job descriptions and candidate profiles into mathematical representations (embeddings) that capture meaning. Instead of matching the keyword "Python developer," it understands the concept and also finds candidates who describe themselves as "backend engineers" with Python projects, data scientists using Python, or similar roles. This is the technology behind platforms like Taleva.

Ready to put these concepts into practice? Book a Taleva demo and see semantic AI sourcing in action across 15+ European talent sources.