Annexure · AI Glossary & Prompt Components

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Glossary of Artificial Intelligence Terms

Key terms and concepts used throughout the course, explained in simple language.

Artificial Intelligence (AI)
The simulation of human intelligence processes by machines, including learning, reasoning, and self-correction.
Artificial General Intelligence (AGI)
A hypothetical AI system with cognitive abilities equal to a human across all domains.
Machine Learning (ML)
A subset of AI where algorithms learn patterns from data without explicit programming.
Computer Vision (CV)
The field of AI that enables machines to interpret and understand visual information from images and videos.
Natural Language Processing (NLP)
The branch of AI focused on enabling computers to understand, interpret, and generate human language.
Knowledge Engineering
The process of building knowledge-based systems by capturing and structuring domain expertise.
Robotics
The interdisciplinary field combining AI, engineering, and mechanics to design intelligent machines that can sense and act.
Bias & Fairness
The study and mitigation of systematic errors in AI systems that can lead to unfair outcomes for certain groups.
Transparency
The principle that AI decisions should be interpretable and understandable to humans.
Privacy
The protection of user data and ensuring AI systems respect individual privacy rights.
Generative AI
AI models that create new content (text, images, code, audio) based on training data and user prompts.
Prompt Engineering
The practice of designing and refining prompts to effectively guide AI models toward desired outputs.
ChatGPT
A conversational AI model developed by OpenAI, capable of generating human-like text responses.
Hugging Face
A platform hosting thousands of pre-trained AI models, datasets, and tools for NLP and other domains.
Perplexity
An AI-powered search engine that provides concise answers with cited sources.
Few-Shot Learning
A prompting technique where the AI is given a few examples to guide its response pattern.

Prompt Components · Complete Reference

The 10 essential components for crafting effective prompts. Use this as a checklist when building prompts for AI models.

S.N Component What to Include Example (Insert this part in prompt)
1 Role / Persona Assign who the AI should act as Act as an AI researcher / Act as a college professor / Act as a Hugging Face expert
2 Task / Instruction Clearly state what you want the AI to do Explain / Summarize / Compare / List / Generate an image / Write code
3 Context / Background Give necessary situation or data You are reading a 2025 research paper on Generative AI…
4 Format / Structure Specify exact output style in 5 bullet points / in a table / in exactly 100 words / numbered list
5 Constraints / Limits Word count, difficulty, language, tone, length maximum 80 words / for 1st-year students / in simple English / formal tone
6 Examples (Few-Shot) 1–3 input–output examples Q: What is NLP? A: Natural Language Processing… Now define Computer Vision
7 Reasoning Instruction Ask to think step-by-step or justify Think step by step / First reason then answer / Justify each point
8 Tool / Platform Mention specific tool if required Using only Hugging Face / With Perplexity AI / In Google Gemini
9 Iteration / Refinement Ask to improve a previous answer Make it shorter and more academic / Add one real-world example
10 Self-Check / Verification Ask AI to verify its own answer After answering, double-check your response for accuracy

Pro Tip: Combine multiple components for better results. For example:
Act as a college professor · Explain · in 5 bullet points · for beginners · verify your answer

Back to Syllabus Unit 1: AI & Subfields Unit 2: Applications Unit 3: Bias & Fairness Unit 4: Generative AI Unit 5: Prompt Applications PromptLab Prompt Forge