AI & Technology
LLM Large Language Model
A type of AI model trained on large text datasets to predict and generate language. In HR, LLMs are most useful when given structured inputs and constrained to specific tasks — not as open-ended advisors.
RAG Retrieval-Augmented Generation
A technique where the model answers only from specific documents retrieved at query time, rather than relying on its training data. Prevents hallucination and ensures answers are traceable to a source. Essential for policy Q&A.
Hallucination
When an AI model generates plausible-sounding but factually incorrect or invented content. A primary risk in unconstrained HR use — especially policy interpretation and compliance drafting.
Confabulation
A specific form of hallucination where the model fills in missing information with invented details that fit the context. In analytics, this can produce syntactically valid SQL queries that reference nonexistent tables or return fabricated findings.
Structured Output
AI-generated content formatted as labeled fields, tables, or a defined schema — rather than free-flowing prose. Structured outputs are easier to review, compare, store, and audit, and reduce the risk of silently misread results.
Schema
A defined structure specifying what fields an output must contain and in what form. In HR prompts, enforcing a schema (e.g., Strengths / Development areas / Evidence) makes outputs consistent and comparable across different runs.
SQL Structured Query Language
A standard language for querying relational databases. In the analytics domain, AI translates plain-English business questions into SQL queries — which must be reviewed by an analyst before executing against production data.
Natural Language → SQL
The process of converting a plain-English question (e.g., "which departments had the most attrition?") into an executable SQL query. AI-generated SQL must always be validated against the actual database schema before running.
Corpus
The full collection of source documents the model is working from — for example, an organization's complete set of HR policies. In RAG workflows, answers are grounded in the corpus only, not in the model's general training.
Sentiment Analysis
The automated classification of text as positive, negative, or neutral in tone. Used in survey analysis to surface employee concerns or recurring morale signals at scale without reading every response manually.
Theme Extraction
Clustering large sets of open-ended responses into recurring topics. Useful for employee surveys where qualitative patterns need to be identified and escalated without manual coding of each response.
Prompt Architecture
High-Precision Prompt
A prompt that includes a defined role, a bounded task, explicit inputs, hard constraints, and a reusable output schema. The opposite of an open-ended question — designed to produce consistent, auditable results every time.
Role (prompt component)
The persona or function assigned to the model at the start of a prompt — e.g., "You are an HR policy assistant." Sets perspective and constrains the model's frame of reference before it processes any instructions.
Task (prompt component)
The specific action the model is asked to perform — e.g., "Convert the following manager notes into a structured evaluation." Should describe a concrete transformation, not a vague goal.
Inputs (prompt component)
The specific material the model should work from — policy text, manager notes, survey responses, database schemas, or retrieved context. Bounding the model to named inputs prevents it from drawing on general training knowledge.
Constraints (prompt component)
Explicit rules that limit what the model may do — e.g., "do not speculate," "cite the section title," "do not invent information." The primary mechanism for preventing hallucination, legal drift, and unjustified conclusions.
Format (prompt component)
The required output structure — e.g., specific labeled fields, a table, or a numbered list. Enforcing format makes outputs consistent across different users and runs, and easier to store and compare.
Constrained Workflow
A task design where the model operates within a defined role, bounded task, explicit inputs, non-negotiable constraints, and a reusable output structure. The core architectural pattern recommended throughout this guide.
Operational Consistency
Every instance of the same task produces output in the same structure and tone, regardless of which team member runs it. Reduces variation and improves fairness across HR processes.
Transformation (vs. Generation)
Using AI to convert existing material (manager notes, policy text, survey data) into a cleaner structured form — rather than generating new content from scratch. Transformation is safer and more consistent because the inputs constrain the outputs.
Prompt Template
A reusable prompt structure with placeholder fields (e.g., {role}, {policy text}) that can be filled in for each specific use. Templates enforce consistency and make quality easier to manage at scale.
HR Practice
OKRs Objectives and Key Results
A goal-setting framework where each objective is paired with 3–5 specific, quantifiable outcomes used to measure progress. In the performance domain, AI converts aspirational or vague goals into properly structured OKRs.
HRBP HR Business Partner
An HR professional embedded with or closely aligned to a specific business unit. HRBPs often act as the bridge between HR data, analytics output, and leadership decisions — a key human review role in AI-assisted workflows.
Calibration
The process by which managers and HR leaders align on performance standards, ratings, and language to reduce individual inconsistency. AI assists calibration by normalizing review language before calibration sessions.
Feedback Normalization
Converting variable, informal, or emotionally loaded manager notes into a consistent structured format with labeled fields for strengths, development areas, and evidence. Reduces linguistic inconsistency across review cycles.
Bias Detection
Automated scanning of review text for gendered language, vague criticism, emotionally loaded phrases, or unequal standards across comparable employees. AI surfaces potential bias — it does not resolve it; human judgment remains required.
Bias Laundering
A risk where AI cleans up the surface language of biased writing without removing the underlying bias — making discrimination less visible rather than less harmful. A primary concern in the performance domain.
Skill Gap Analysis
Comparing an employee's current skills against a target role profile to identify missing competencies and prioritize development actions by urgency (high / medium / low). Used in L&D for role transitions, promotions, and capability reviews.
Microlearning
Short, focused instructional units — typically 5–10 minutes — covering one concept, structured as explanation, real-world example, quiz questions, and an applied task. Easier to deploy at scale than full training modules.
Scenario-Based Training
Structured workplace situations with defined roles, decision points, and consequence paths — used to practice judgment and behavior before real situations arise. AI can generate contextually realistic scenarios from a topic and role prompt.
Knowledge Base Structuring
Transforming unstructured HR documents into labeled summaries, key rules, definitions, and FAQ-ready entries for internal knowledge systems. Turns static policy documents into retrievable, structured knowledge.
Attrition
The rate at which employees leave an organization, voluntarily or involuntarily. A key metric in HR analytics — AI can help translate leadership questions about attrition trends into SQL queries or structured summaries.
Tenure Band
A grouping of employees by length of service (e.g., 0–1 year, 1–3 years, 3+ years). Used in workforce analytics to segment attrition, engagement, or compensation data by time-in-role.
Human Review Gate
A mandatory step where a qualified HR professional, analyst, or legal expert reviews and approves AI output before it is acted upon, filed, or communicated. Non-negotiable in compliance, performance decisions, and any output with legal consequence.
Compliance Gap Analysis
Systematically comparing an internal policy against a regulatory summary to identify missing provisions, misaligned language, and required changes. AI accelerates the comparison — but a qualified reviewer must confirm all findings before action is taken.
Policy Simplification
Rewriting technically accurate but operationally unreadable policy language into plain, employee-facing prose without altering the legal meaning. Improves comprehension and adoption of internal policies.
Stale Context
A situation where the document retrieved for a RAG response is outdated — causing the model to answer confidently from superseded or incorrect policy. A primary risk in the onboarding domain when knowledge bases are not actively maintained.