The AI Marketing Glossary: 50 Terms Every Growth Team Should Know
AI has introduced a new vocabulary into marketing: LLMs, RAG, prompt engineering, embeddings, enrichment waterfalls. If you're leading a growth team in 2026, understanding this terminology is no longer optional.
This glossary defines 50 essential AI marketing terms—from foundational concepts to tactical implementations. Each term includes: definition, practical application, and (when relevant) which GENESIS module uses it.
A
1. Agent (AI Agent)
An autonomous AI system that performs tasks without constant human input. Unlike chatbots that respond to prompts, agents plan multi-step workflows, use tools (APIs, databases), and make decisions based on goals. Example: An AI agent that researches companies, enriches emails, and sends personalized outreach sequences. Related module: The Hunter (prospecting agent).
2. API (Application Programming Interface)
A way for two software systems to communicate. AI marketing tools use APIs to connect models (Claude, GPT-4) with data sources (CRM, analytics) and actions (send email, publish article). Example: GENESIS uses the Anthropic API to send prompts to Claude and receive generated content. Related module: All GENESIS modules.
3. A/B Testing (Split Testing)
Comparing two versions (A vs. B) to see which performs better. AI enables rapid A/B testing by generating 50+ variations in minutes instead of manually creating 2-3. Example: PRISM generates 50 ad headline variations, you test the top 10 to find the winner. Related module: PRISM (ad creative).
B
4. Bounce Rate (Email)
Percentage of emails that fail to deliver. "Hard bounce" means invalid email address; "soft bounce" means temporary issue (inbox full). AI enrichment tools reduce bounce rates by validating emails before sending. Example: The Hunter's enrichment waterfall achieves 92% valid email rate (8% bounce). Related module: The Hunter.
5. Brand Voice
The consistent tone, style, and personality of a brand's communications. Challenge with AI: Maintaining brand voice across generated content requires custom prompts or fine-tuning. Example: ORBIT uses brand-specific prompts to maintain PapaPrevoit's "expert but approachable" tone across 300+ articles. Related module: ORBIT.
C
6. ChatGPT
OpenAI's conversational AI product built on GPT models. Most accessible AI tool for non-technical users. Limitation: Designed for conversation, not task automation (use API for workflows). Example: Marketing teams use ChatGPT for brainstorming; GENESIS uses GPT-4 API for production workflows.
7. Claude
Anthropic's family of AI models (Haiku, Sonnet, Opus). Known for long context windows (200K+ tokens) and strong reasoning. GENESIS primarily uses Claude for content generation and complex analysis. Related module: ORBIT (content), The Hunter (analysis).
8. Cold Email
Unsolicited outreach to prospects who haven't expressed interest. AI improves cold email with: personalization at scale, enrichment for targeting, and automated follow-ups. Example: The Hunter sends AI-personalized cold emails with 23% reply rate. Related module: The Hunter.
9. Context Window
The amount of text an AI model can process at once, measured in tokens. Larger context = more information for decisions. Claude Opus: 200K tokens (~150K words). GPT-4: 128K tokens. Example: ORBIT can analyze 10 competitor articles at once to generate better content. Related module: ORBIT.
10. Conversion Rate
Percentage of visitors who complete a desired action (fill form, click CTA, purchase). AI impacts conversion via: personalized copy, A/B testing velocity, and optimized funnels. Example: BP Corp brands average 2.8% visitor-to-lead conversion rate. Related module: All.
D
11. DALL-E
OpenAI's image generation model. Used in marketing for: ad creative visuals, social media images, landing page graphics. Limitation: Text-in-image generation still unreliable. Example: PRISM uses DALL-E for ad creative image variations. Related module: PRISM.
12. Data Enrichment
Adding information to existing records. In B2B: turning "Company Name" into company name + industry + size + revenue + employee count + decision-maker emails. AI automates enrichment by aggregating multiple data sources. Related module: The Hunter.
13. Deliverability (Email)
The ability of emails to reach inboxes (not spam folders). AI affects deliverability via: personalization (reduces spam flags), sending patterns (avoids bulk sending), and email validation (removes invalid addresses). Example: The Hunter validates emails before sending to maintain high deliverability. Related module: The Hunter.
14. Drift (Model Drift)
When an AI model's performance degrades over time due to changing data patterns. In marketing: Content trends change, brand voice evolves, competitor landscape shifts. Solution: Regular prompt updates and retraining. Example: GENESIS prompts are versioned and updated quarterly.
E
15. Embeddings
Mathematical representations of text as vectors (lists of numbers). Enable AI to understand semantic similarity ("cheap" and "inexpensive" have similar embeddings). Used for: semantic search, content clustering, recommendation. Example: ORBIT uses embeddings to find similar articles for internal linking. Related module: ORBIT.
16. Enrichment Waterfall
Strategy of querying multiple data providers in sequence until valid data is found. Increases data coverage and accuracy. Example: The Hunter checks Apollo → Hunter.io → Dropcontact → Prospeo until valid email is found. Related module: The Hunter. Full waterfall strategy →.
F
17. Few-Shot Learning
Giving an AI model a few examples (2-5) in the prompt to guide output format. More examples = better results, but uses more tokens. Example: Show Claude 3 example outreach emails → it generates new emails in that style. Related module: The Hunter, ORBIT.
18. Fine-Tuning
Training an AI model on custom data to specialize it for specific tasks. More expensive than prompting, better for repetitive tasks with consistent format. Example: Fine-tuning GPT-4 on 500 brand articles to match brand voice perfectly. Related module: Not currently used in GENESIS.
19. Flux
Black Forest Labs' open-source image generation model. Alternative to DALL-E/Midjourney. Used in GENESIS for: logo generation, ad creative images. Related module: PRISM, BrandArchitect.
20. Foundation Model
A large AI model trained on broad data, designed to be adapted for many tasks. Examples: GPT-4, Claude, Gemini. Contrast with specialized models (only do one thing). Related module: All GENESIS modules use foundation models.
G
21. GPT (Generative Pre-trained Transformer)
OpenAI's model architecture. GPT-4 is the current flagship. "Generative" = creates new content. "Pre-trained" = trained on massive datasets. "Transformer" = the neural network architecture. Related module: PRISM (speed-focused tasks).
22. GSC (Google Search Console)
Google's tool for monitoring site performance in search results. AI SEO tools integrate with GSC to: track rankings, identify keyword opportunities, optimize content. Example: ORBIT connects to GSC to find ranking articles that need updates. Related module: ORBIT.
H
23. Hallucination
When an AI model generates false or fabricated information confidently. Common in: statistics, dates, URLs, technical facts. Solution: Fact-checking, RAG (retrieval augmented generation), human review. Example: Claude might invent a study that doesn't exist—ORBIT requires human review for factual accuracy.
24. Hunter.io
B2B data provider for finding and verifying email addresses. Used in enrichment waterfalls. Example: The Hunter uses Hunter.io as second provider in enrichment waterfall. Related module: The Hunter.
I
25. Intent Data
Information about prospects' behavior indicating purchase readiness. Examples: Visiting pricing page, downloading whitepaper, searching for "alternatives to [competitor]". AI uses intent data to: prioritize outreach, personalize messaging. Example: The Hunter prioritizes prospects who visited competitor review sites. Related module: The Hunter.
26. Iteration (Prompt Iteration)
Refining prompts through testing to improve output quality. Process: Test prompt → Review output → Adjust prompt → Repeat. Example: ORBIT's article generation prompt went through 47 iterations before reaching production quality. Related module: All.
J
27. JSON (JavaScript Object Notation)
A data format for structured information. AI models often output JSON for easy parsing. Example: The Hunter receives enrichment data as JSON: {"email": "john@company.com", "valid": true, "source": "Hunter.io"}. Related module: The Hunter, ORBIT.
K
28. Keyword Clustering
Grouping related keywords into topic clusters. AI automates clustering by analyzing semantic similarity. Example: "life insurance", "term life insurance", "whole life insurance" cluster into one article topic. Related module: ORBIT.
29. Keyword Difficulty (KD)
A metric (0-100) indicating how hard it is to rank for a keyword. Lower = easier. AI SEO tools analyze KD to prioritize content creation. Example: ORBIT targets keywords with KD < 40 for faster ranking. Related module: ORBIT.
L
30. LLM (Large Language Model)
An AI model trained on vast amounts of text to understand and generate language. Examples: GPT-4, Claude, Gemini. LLMs power most AI marketing tools. Related module: All GENESIS modules are built on LLMs.
31. Lead Scoring
Assigning numerical scores to leads based on likelihood to convert. AI improves scoring by analyzing patterns across thousands of leads. Example: PULSE (BP Corp's lead distribution platform) uses AI scoring to route high-value leads to premium buyers.
32. Localization
Adapting content for different languages and cultures. AI enables rapid localization by translating and culturally adapting content. Example: BP Corp operates brands in French, Hungarian, English (UK), English (US) using AI translation. Related module: CAST (multi-language video).
M
33. Midjourney
Popular image generation AI. Known for artistic, high-quality visuals. Used in marketing for: concept art, social media images, ad creative. Alternative to DALL-E and Flux. Related module: Not currently used (GENESIS uses Flux).
34. Model Router
A system that selects which AI model to use for each task based on requirements. Example: GENESIS routes to Claude for long content, GPT-4 for speed, Gemini for cost. Related module: All (GENESIS has built-in model routing).
35. Multi-Modal
AI that processes multiple data types: text, images, audio, video. Example: Claude can analyze screenshots, GPT-4 Vision can extract text from images. Related module: PRISM (analyzes visual ad creative).
N
36. NLP (Natural Language Processing)
The field of AI focused on understanding and generating human language. All LLMs are NLP systems. Used in marketing for: content generation, sentiment analysis, chatbots. Related module: All.
O
37. One-Shot Learning
Giving an AI model one example in the prompt to guide output. Less robust than few-shot, but uses fewer tokens. Example: "Here's one good headline, write 20 more like it." Related module: PRISM.
38. Outreach Sequence
A series of emails sent over time to prospects. AI generates sequences with: personalized first emails, timed follow-ups, conditional logic (if replied, stop; if not, send next). Example: The Hunter sends 5-email sequences with 23% reply rate. Related module: The Hunter.
P
39. Personalization (at Scale)
Customizing content for individuals using data and AI. Example: AI generates outreach emails mentioning each prospect's company, role, and recent news. The Hunter personalizes 500 emails/hour. Related module: The Hunter.
40. Prompt Engineering
The skill of crafting effective instructions (prompts) for AI models. Good prompts = better output. Includes: clarity, examples, constraints, format specifications. Example: ORBIT's article generation prompt is 800 words long with examples and constraints. Related module: All.
41. Programmatic SEO (pSEO)
Automatically generating hundreds/thousands of SEO pages using templates and data. AI enables pSEO by: generating unique content per page, optimizing for keywords, maintaining quality. Example: ORBIT generated 900+ unique articles across 13 brands. Related module: ORBIT.
R
42. RAG (Retrieval Augmented Generation)
An AI technique that retrieves relevant information from a database before generating a response. Reduces hallucinations by grounding responses in real data. Example: ORBIT retrieves competitor article content before writing, ensuring factual accuracy. Related module: ORBIT.
43. Reply Rate
Percentage of outreach emails that receive responses. Industry average for cold email: 8-12%. AI-personalized outreach: 20-30%. Example: The Hunter achieves 23% reply rate with AI-generated personalized sequences. Related module: The Hunter.
44. RLS (Row-Level Security)
Database security that restricts which rows users can access. Important for multi-tenant AI platforms. Example: PULSE uses RLS to ensure clients only see their own leads. Related module: PULSE (not GENESIS, but related).
S
45. Semantic Search
Search based on meaning, not just keyword matching. AI uses embeddings to understand "cheap" and "inexpensive" as similar. Example: ORBIT finds semantically similar articles for internal linking, even if keywords differ. Related module: ORBIT.
46. Sentiment Analysis
AI analyzing text to determine emotional tone (positive, negative, neutral). Used in marketing for: social listening, review analysis, brand monitoring. Example: Analyzing customer feedback to identify pain points. Related module: Not currently in GENESIS (on roadmap).
47. System Prompt
The initial instruction given to an AI that defines its role and behavior. Sets context for all subsequent prompts. Example: ORBIT's system prompt: "You are an expert SEO content writer specializing in lead generation verticals." Related module: All.
T
48. Token
The unit AI models use to process text. ~1 token = 4 characters or 0.75 words. API pricing is per token. Example: A 2,000-word article = ~2,700 tokens. Claude API: $3/million input tokens. Related module: All (cost management).
49. Temperature (Model Parameter)
A setting (0.0-1.0) controlling AI output randomness. Low temperature (0.1) = consistent, predictable. High temperature (0.9) = creative, varied. Example: ORBIT uses temperature 0.3 for factual articles, 0.7 for creative ad copy. Related module: All.
V
50. Vertical (Market Vertical)
A specific industry or market segment. BP Corp operates in 9 verticals: life insurance, income protection, home security, solar, renovation, career change, wealth management, retirement, health insurance. AI marketing platforms often specialize by vertical. Related module: All GENESIS modules are vertical-aware.
How to Use This Glossary
For Beginners
Start with foundational terms: LLM (#30), Prompt Engineering (#40), API (#2), Token (#48), Temperature (#49). These form the basis for understanding how AI marketing tools work.
For Practitioners
Focus on tactical terms: Enrichment Waterfall (#16), RAG (#42), Few-Shot Learning (#17), Programmatic SEO (#41), Reply Rate (#43). These guide implementation decisions.
For Leaders
Prioritize strategic terms: Foundation Model (#20), Model Router (#34), Agent (#1), Vertical (#50), Lead Scoring (#31). These inform tool selection and team structure.
The AI Marketing Vocabulary Gap
In BP Corp's interviews with 47 GENESIS early adopters, 73% reported "vocabulary confusion" as a barrier to AI adoption. Terms like RAG, embeddings, and fine-tuning are thrown around in sales calls without clear definitions.
This glossary is the reference we wish existed when we started building GENESIS. Share it with your team, bookmark it, reference it when evaluating AI tools.
The teams that win in AI marketing aren't necessarily the most technical—they're the ones that understand the concepts well enough to ask the right questions and make informed decisions.
Ready to put these terms into practice? See how GENESIS implements LLMs, RAG, enrichment waterfalls, and programmatic SEO in one unified platform. View pricing →