Artificial intelligence, LLMs, generative AI, prompt engineering. The list goes on.
You don’t need to follow every AI headline to see that this language is quickly becoming part of the conversation. And when you’re busy practicing law, deciphering tech-speak isn’t high on your to-do list.
That said, understanding a few key terms can help you evaluate tools, ask sharper questions, and make informed decisions about how AI could fit into your firm.
Consider this your plain-English guide to the AI terms you’re most likely to encounter in emails, articles, webinars, product demos, and conversations about legal technology.
Smokeball's AI Glossary
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Algorithm
A set of instructions a computer follows to complete a task or solve a problem.
Algorithmic Bias
When an AI system produces unfair or skewed results because of the information it was trained on.
Artificial Intelligence (AI)
Technology that helps computers perform tasks that normally require human thinking, such as understanding language, recognizing patterns, making recommendations, or generating content.
Automation Agent
Technology that performs repetitive tasks automatically, reducing the need for manual work.
Chatbot
A tool that uses AI to have conversations and answer questions.
ChatGPT
An AI assistant created by OpenAI that can answer questions, draft content, summarize information, and more.
Claude
An AI assistant created by Anthropic that can answer questions, draft content, summarize information, and more.
Cybersecurity
The safeguards used to protect systems, data, and information from unauthorized access, breaches, or cyberattacks.
Data Privacy
How information is collected, stored, shared, and protected. For law firms, this often relates to whether client or confidential information is being handled safely.
Deepfake
An AI-generated image, video, or audio recording that appears real but has been manipulated or completely created by AI.
Encryption
A security measure that helps protect data from unauthorized access.
Ethics
The professional responsibilities and considerations involved in using AI, including issues such as confidentiality, competence, supervision, and accuracy.
Fine-Tuning
Additional training given to an AI model to make it better at a specific task, industry, or type of work.
Foundation Model
A large AI model trained on broad information that serves as the foundation for tools like ChatGPT, Claude, and other AI applications.
Generative AI
AI that creates new content, such as text, images, summaries, audio, or ideas.
Hallucination
When AI confidently provides information that is incorrect, made up, or misleading.
Large Language Model (LLM)
The technology behind tools like ChatGPT. LLMs are trained on large amounts of text and can understand and generate human language.
Machine Learning (ML)
A type of AI that learns patterns from data instead of being programmed with specific rules.
Model
The AI system itself. A model is what has been trained to recognize patterns and generate responses.
Natural Language Processing (NLP)
Technology that helps computers understand and respond to human language.
Open Source
Software whose underlying code is publicly available for anyone to review, modify, or improve—often where data privacy is of higher concern.
Predictive AI
AI that uses patterns to make recommendations or predictions about what might happen next.
Predictive Analytics
Technology that uses historical data and AI to help forecast future outcomes or trends.
Prompt
The question, instruction, or request you give an AI tool.
Prompt Engineering
The practice of refining prompts to get better, more accurate, or more useful responses from AI.
Responsible AI
The practice of developing and using AI in a way that is ethical, secure, transparent, and trustworthy.
Semantic Search
Search technology that focuses on meaning and intent—not just keywords—to find more relevant information.
Token
A small piece of text AI uses to process language. Think of tokens as the building blocks AI reads and writes.
Training Data
The information AI learns from before it can answer questions or complete tasks.
Transparency
The ability to understand how an AI system reached a recommendation, conclusion, or response.
You don’t need to memorize every AI term to benefit from it. Knowing the basics simply makes it easier to understand the tools available to you and how they can support your work.
