Resources
AI glossary in practice
Shared language for TA, HR, and AI workflows. Pick a letter, search, or open a term for a longer read and FAQs. For deeper guides see the blog.
A
Adverse impact
The 4/5ths rule does not care what you intended: measure your funnel by protected group before an auditor does it for you.
Read definition →Agent knowledge base
Think internal wiki the model is allowed to read: small, current files beat a dump of every PDF you ever saved.
Read definition →AI adoption ladder
Know where you sit so you do not skip foundations: weak chat prompts do not fix themselves when you bolt on Make or n8n.
Read definition →AI bias audit
DraftAudit before contract signature: the patterns an AI inherited from historical data are invisible until you measure group outcomes.
Read definition →AI slop
Fix the input, not only the model: saved tone rules and short prompts beat a twelve-paragraph essay nobody answers.
Read definition →AI-native
Treat AI like infrastructure: prompts, skills, Markdown knowledge bases, and reviews so hiring work scales without heroics.
Read definition →Async screening
Useful after automated outreach spikes volume; design for clarity, time boxes, and human review of edge cases.
Read definition →
B
C
F
H
Hallucination
Fluency is not truth: treat every fact-shaped line as guilty until a human or a checked source acquits it.
Read definition →Human-in-the-loop (HITL)
HITL is not vague supervision: it is a choke point with owners, SLAs, and evidence that review happened before irreversible hiring actions ship.
Read definition →
L
Large language model (LLM)
Pick the depth that matches the job: great copy needs a different bar than production integrations with your ATS.
Read definition →LLM tokens
Long PDFs and pasted threads are expensive in money and attention: lean inputs usually behave better than dumping whole exports.
Read definition →
M
P
Prompt chain
Break hiring artifacts into stages so each prompt stays small, testable, and easy to fix when one link fails.
Read definition →Proprietary talent pool
Moat is relationships plus structured notes, not a bigger CSV export: refresh consent, tags, and who last spoke with each person.
Read definition →
R
S
Scorecard
Give models and humans the same grid: traits, anchors, and proof signals instead of vibe checks alone.
Read definition →Semantic search
Use meaning-based ranking when titles vary; keep Boolean for hard gates and compliance filters.
Read definition →Structured output
Turn "a paragraph of vibes" into score plus rationale columns your sheet or ATS can consume.
Read definition →System instructions
Treat it like onboarding a colleague: company, channel rules, and examples live once, then short prompts still produce on-brand outreach.
Read definition →
T
Talent acquisition (TA)
TA owns the system around hiring: policy, tooling choices, and recruiter craft, especially when AI enters the stack.
Read definition →Talent acquisition metrics
DraftPick four or five numbers your ATS already surfaces. Tracking twenty metrics nobody acts on creates noise instead of signal.
Read definition →
W
Also see AI tools, guides by role, and free resources.