As the practice of AI Whispering continues to evolve, so does its shared vocabulary.
This glossary defines the essential terms that shape the human evolution of working with intelligent systems—from Atomic Rituals to Virtuous Cycles.
It serves as a quick reference for leaders, engineers, and creators seeking to understand not just the technology, but the mindset, ethics, and language of collaboration at the heart of this new era of Human–AI partnership.
AI Ethics
Principles and practices that align AI intent, impact, and integrity. Includes fairness, transparency, privacy, safety, accountability, and human oversight.
AI Governance
Policies, roles, and controls that guide how AI is selected, deployed, monitored, and audited across an organization.
AI Hallucination
Confident but incorrect or fabricated output from a model. Reduced by grounding, constraints, retrieval, and strong evaluation.
AI Observability
End-to-end visibility into model/data health (latency, failure modes, drift, safety flags) to support rapid diagnosis and remediation.
AI Pair Programming
Working with an AI assistant during design, coding, and review to accelerate exploration, increase coverage, and improve code quality.
AI Whisperer
A human partner who guides intelligent systems with clarity, ethics, and craft—translating intent into high-quality outcomes and learning loops.
AI Whispering
The human practice of engaging, collaborating, and co-creating with intelligent systems—prioritizing relationship quality over raw output.
Alignment (Model Alignment)
The extent to which a model’s behavior matches human intent, organizational policy, and societal norms.
Agent (AI Agent)
A system that can plan, call tools/functions, and take multi-step actions toward goals under constraints and feedback.
API (Application Programming Interface)
A contract for programmatic access to services (models, data, tools) used within AI applications and automations.
AST (Abstract Syntax Tree)
A structured representation of source code used by compilers, linters, and some code-gen analyzers to reason about edits.
Atomic Rituals
Small, repeatable practices that compound learning and change (e.g., prompt journals, daily evals, micro-retros).
Bias (Model/Data Bias)
Systematic distortion in data or behavior that yields unfair outcomes. Managed via data curation, evaluation, and governance.
Chain of Thought (CoT)
Prompting that elicits intermediate reasoning steps. Use responsibly; prefer structured reasoning or tool-assisted traces when auditing.
CI/CD (Continuous Integration / Continuous Delivery)
Automation that merges, tests, and ships changes rapidly; increasingly includes AI-aware tests and guardrails.
Context Window / Tokens
The maximum text a model can attend to at once, measured in tokens. Drives prompt design, chunking, and retrieval strategies.
Continual Learning (Human–AI)
A reciprocal loop where humans learn each cycle and artifacts later inform fine-tuning or new systems—practical “continual” learning today.
Data Leakage
Sensitive data unintentionally exposed to systems/users or used in training. Prevent with redaction, policy, and vaulting.
Deployment (Model/App)
Packaging and serving AI capabilities reliably (APIs, latency SLOs, autoscaling, caching, rollback).
Determinism / Non-Determinism
Repeatability of outputs. Controlled by temperature, sampling, seeding, and constraints.
Diff-Aware Editing
Constrained edits that touch only intended regions, minimizing regressions (crucial for safe code-gen).
Drift (Data/Model Drift)
Shifts in input distributions or behavior over time that degrade quality. Detect and mitigate via monitoring and retraining.
Embeddings
Numeric vectors representing meaning; power semantic search, clustering, deduplication, and RAG retrieval.
Evals (Evaluation Suites)
Repeatable tests that measure correctness, safety, robustness, and UX quality across versions and prompts.
Few-Shot / Zero-Shot
Providing few or no labeled examples in the prompt. Few-shot can “teach” local patterns without retraining.
Fine-Tuning
Further training a base model on curated data to specialize tone, tasks, or domains.
Function Calling / Tool Use
Letting a model invoke external tools (APIs, databases) via structured outputs—key to reliable agents.
Generative AI
Models that produce new content (text, code, images, audio, video) from learned patterns.
Grounding
Constraining model answers to verified sources (docs, databases) to reduce hallucinations and improve trust.
Guardrails
Runtime constraints (policies, validators, regex/JSON schemas, content filters) that enforce safety and format.
HITL (Human-in-the-Loop)
Humans review/steer AI at critical points to ensure quality, safety, and learning.
Human Transformation
Evolving mindsets, skills, and ethics to work with intelligent systems, not just through them.
Inference
Running a trained model to produce outputs. Performance depends on hardware, batching, caching, and request shape.
Intelligent Systems
Software that recognizes/generates patterns, increasingly multi-modal and tool-using.
Jailbreak / Prompt Injection
Adversarial inputs that coerce models to violate policy or exfiltrate secrets. Counter with content filters, isolation, and robust retrieval.
Latency / Throughput
How fast and how much a system serves. Tuned via batching, caching, model size, and parallelism.
Learned Resilience
A cycle that metabolizes setbacks into insight via reflection, reframing, and small next moves.
LLM (Large Language Model)
A model trained on massive corpora to predict tokens and perform language tasks; foundation for most code/text assistants.
MLOps / LLMOps
Operational discipline for managing models: data, training, deployment, monitoring, governance, rollback.
Model Card
A documented summary of a model’s data sources, limits, risks, and intended uses.
Multimodal
Models that handle multiple input/output types (text, images, audio, video) and their combinations.
Nucleus Sampling / Top-p, Temperature
Decoding controls that balance creativity and precision. Lower values = safer, more deterministic outputs.
Observability (AI/LLM)
See AI Observability.
On-Policy / Off-Policy Feedback
Learning signals gathered during usage (on-policy) vs curated offline datasets (off-policy).
Orchestration
Coordinating prompts, tools, retrieval, memory, and control flow in multi-step AI applications.
PII (Personally Identifiable Information)
Data that can identify a person; requires strict handling, minimization, and access controls.
Prompt Engineering
Designing instructions, context, and constraints to elicit reliable outputs—distinct yet complementary to AI Whispering.
An adversarial input that tries to override an AI system’s original instructions or safety constraints. A jailbreak is a variant that tricks a model into producing content or actions outside its intended scope—by hiding hidden commands, exploiting context-window limits, or re-framing the conversation.
Prompt Template / System Prompt
Reusable prompt frames and the governing instruction that sets model behavior and tone.
RAG (Retrieval-Augmented Generation)
Combines search over trusted content with generation, grounding answers in your sources.
Reasoning Models
Models and settings specialized for multi-step problem solving; often slower but more reliable on complex tasks.
Refactoring (AI-Assisted)
Restructuring code for clarity/performance without changing behavior, with AI proposing diffs and tests.
Reflection Rituals
Lightweight practices (retros, 5-Whys, P5) that convert speed into learning and guard against drift.
Safety Classifier / Content Filter
A model or rule set that detects disallowed or risky content before or after generation.
Sampling (Decoding)
How tokens are chosen at inference (greedy, nucleus, beam). Impacts style, diversity, and accuracy.
SDLC (Software Development Life Cycle)
End-to-end process (plan-build-test-release-operate). With AI, includes data pipelines, evals, safety reviews, and post-deployment learning.
Semantic Search
Finding meaningfully similar content using embeddings vs keyword matching.
SolveIt Mindset
Small, testable steps with immediate feedback—craftsmanship over acceleration; attention over volume.
Strategic Inflection Point
A market/technology shift that changes operating rules; requires new mental models and structures.
System Prompt Hardening
Defenses that preserve intent against prompt injection (role separation, content isolation, output validation).
Systemic Thinking
Seeing interdependencies across people, process, policy, and platforms; choosing interventions that improve the whole.
Test Pyramid (AI-Aware)
Unit → integration → scenario → evals; adds red-team and safety tests for AI behavior.
Token
A chunk of text the model processes. Pricing and context limits are token-based.
Trace / Audit Log
Captured inputs, outputs, tool calls, and decisions for debugging, compliance, and learning.
Vector Database
A store optimized for embedding vectors to power fast semantic search and RAG.
Virtuous Cycle (Human–AI)
A regenerative loop where human insight improves model outputs, which in turn sharpen human understanding.
Vulnerability (Model/App)
Security weaknesses exploitable via inputs (prompt injection), outputs (data exfiltration), or integrations (tool abuse).
AI Whispering
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