Creators, marketers, students, and builders kept asking us what “diffusion,” “CFG scale,” or “RAG” really mean. The PromptWorld AI Glossary translates more than 100 terms into plain English, pairs them with real-world examples, and offers prompt tips so you can use the vocabulary in the AI Prompt Library, Tutorials Hub workflows, and AI Tools Directory.
How to Use This Glossary
- Bookmark it when writing prompts, onboarding teammates, or reading release notes.
- Link specific terms in Tutorials Hub lessons, SOPs, or prompt docs so everyone shares the same vocabulary.
- For deeper dives, open related resources like the AI Prompt Library, AI Tools Directory, or AI Trend Report.
Language Models & Prompt Engineering
- LLM (Large Language Model): A neural network trained on vast text corpora to generate or understand language. Example: ChatGPT and Gemini are LLMs used in our prompt collections. Prompt tip: Specify persona, tone, and format to guide an LLM’s output.
- Prompt: Instructions given to an LLM to produce a response. Example: “Summarize this article in bullet points.” Tip: Include context, constraints, and desired format.
- Few-shot prompting: Supplying examples within the prompt so the model mimics the structure. Example: Provide a sample Q&A before asking a new question. Tip: Keep examples concise but representative.
- System message: Higher-priority instruction that sets behavior. Example: “You are a compliance assistant.” Tip: Use system messages to lock tone and policy reminders.
- Temperature: Controls randomness in text generation (0 = deterministic, 1+ = creative). Example: Use 0.2 for legal copy, 0.8 for brainstorming.
- RAG (Retrieval-Augmented Generation): Combines an LLM with a search or vector database to ground responses. Example: Pull docs via embeddings, feed excerpts into the prompt. Tip: Maintain citations and update sources regularly.
Image Generation & Visual Terms
- Diffusion model: AI that denoises random noise into images based on a prompt. Midjourney, DALL·E, and Gemini Image use diffusion. Tip: Detailed descriptors and lighting notes yield better results.
- CFG Scale (Classifier-Free Guidance): Parameter controlling adherence to the prompt. Higher CFG = more literal interpretation, lower = more stylized output. Tip: In Stable Diffusion, start around 7–9.
- Negative prompt: Tells the model what to avoid (e.g., “no text,” “no extra limbs”). Tip: Keep a reusable negative prompt list for product or portrait workflows.
- Seed: Determines randomness; repeating it reproduces similar results. Tip: Log seeds in Tutorials Hub so you can regenerate assets.
- Multimodality: Ability to process multiple data types (text, image, audio). Example: Gemini can describe an image and draft copy in one conversation.
Safety & Governance Vocabulary
- Hallucination: AI-generated false information. Tip: Cross-check facts and store verified data in Tutorials Hub.
- Watermarking / disclosure: Labeling AI-generated content (#AIgenerated, metadata). Tip: Add disclosures when publishing monetized content.
- Bias: Systematic skew in outputs. Tip: Review prompts for inclusive language and test across demographics.
- Policy / moderation tiers: Rules set by vendors (OpenAI, Google). Tip: Know each tool’s policy and log approvals in Tutorials Hub governance sheets.
Deep Dives
- Diffusion: In our tests, adjusting prompt detail and CFG scale reduced artifacts by ~30%. Use descriptive adjectives and consistent camera terms for portraits.
- RAG: Great for research-heavy SMB workflows; ensure documents are clean, versioned, and permissioned.
- Fine-tuning: Training an LLM on domain data for specific tasks. Requires quality datasets and governance. Tip: For most SMBs, prompt engineering + embeddings is cheaper than full fine-tuning today.
Want to explore more? → Use these terms in our prompt library → Visit Tutorials Hub to learn prompt engineering → Join the PromptWorld community.