Question 1

What is prompt engineering?

Accepted Answer

Prompt engineering is the skill of crafting, testing, and refining prompts to get the best possible responses from AI models. It's part writing, part experimentation, and part understanding how AI models interpret instructions. Good prompt engineers know how to structure instructions clearly, provide the right amount of context, use techniques like few-shot examples and chain-of-thought reasoning, and iterate on their prompts based on the outputs they receive. As AI becomes more central to business workflows, prompt engineering is becoming a core professional skill — not just for developers, but for marketers, support teams, writers, and anyone who works with AI tools regularly.

Question 2

What is an AI agent?

Accepted Answer

An AI agent is an AI system that goes beyond simple question-and-answer interactions. It can make decisions, use tools (like searching the web, running code, or accessing databases), maintain memory across interactions, and work toward goals with minimal human intervention. Think of it as the difference between asking someone a question (a chatbot) and hiring someone to do a job (an agent). A customer support agent doesn't just answer questions — it looks up order information, processes refunds, escalates issues, and follows company policies. Building effective AI agents requires well-crafted system prompts that define the agent's role, available tools, decision-making rules, and boundaries.

Question 3

What is the difference between ChatGPT and Claude?

Accepted Answer

ChatGPT (by OpenAI) and Claude (by Anthropic) are both top-tier large language models, but they have different strengths. ChatGPT is known for its broad capabilities and large plugin ecosystem, while Claude is known for its industry-leading 200K token context window (able to process roughly 500 pages at once), nuanced reasoning, and a focus on safety and honesty. ChatGPT offers GPT-4 through its Plus plan, while Claude offers multiple model sizes (Opus, Sonnet, Haiku). Many professionals use both depending on the task. For long document analysis, Claude often excels; for tasks requiring integrations and plugins, ChatGPT has more options.

Question 4

What is RAG in AI?

Accepted Answer

Retrieval-Augmented Generation (RAG) is a technique that combines AI language models with external data sources. Instead of relying solely on what the AI learned during training (which has a knowledge cutoff date), RAG first retrieves relevant information from a database, document collection, or knowledge base, then feeds that information into the AI's prompt so it can generate an answer grounded in real data. RAG dramatically reduces hallucinations and enables AI to work with private, proprietary, or real-time data that wasn't in its training set. It's the foundation of most enterprise AI applications — customer support bots that access help documentation, internal assistants that search company wikis, and research tools that query academic databases.

Question 5

What are AI hallucinations?

Accepted Answer

Hallucination is when an AI model generates information that sounds confident and plausible but is actually false, made up, or inaccurate. This happens because AI models are trained to produce likely-sounding text, not to verify facts. They can fabricate statistics, cite non-existent research papers, invent historical events, or confidently give wrong answers to factual questions. Hallucinations are one of the biggest challenges in AI deployment. They're particularly dangerous in high-stakes domains like healthcare, legal, and finance where incorrect information can have serious consequences. The best defense is combining AI with retrieval systems (RAG), adding explicit "don't make things up" rules to your prompts, and always verifying critical information.

AI & Prompt Engineering Glossary

AI Agent

Chain-of-Thought Prompting

ChatGPT

Claude

Context Window

Few-Shot Prompting

Fine-Tuning

Gemini

Grounding

Guardrails

Hallucination

Large Language Model (LLM)

Prompt

Prompt Engineering

RAG (Retrieval-Augmented Generation)

System Prompt

Temperature

Tokens

Frequently Asked Questions

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