AI, Machine Learning, Prompt Engineering & Chatbots: A Free Tutorial

Introduction

Artificial intelligence (AI) and machine learning (ML) are transforming nearly every field. Chatbots like ChatGPT are one of the most accessible examples of this transformation: they turn natural‑language prompts into text, code, images or data analysis.

Learning how these systems work and how to communicate effectively with them is a powerful skill for the coming decade.

This tutorial explains the basics of AI and ML, introduces prompt engineering—the practice of crafting effective prompts for language models—and shows why these skills are valuable for your career and personal projects.

1. What is AI, ML and a chatbot?

Artificial intelligence refers to computer systems designed to perform tasks that normally require human intelligence, such as understanding language or recognizing patterns.

Machine learning is a subset of AI where models learn patterns from data. Chatbots like ChatGPT use large language models (LLMs) trained on vast text corpora to generate coherent responses.

A chatbot interacts through a conversational interface. Modern chatbots accept text, voice and even image inputs and can perform tasks such as answering questions, brainstorming ideas, translating languages, summarizing documents and generating code.

To get the most out of a chatbot you need to know how to communicate with it effectively—this is where prompt engineering comes in.

2. What is a prompt?

A prompt is the input you provide to the AI system. The MIT Sloan Teaching & Learning Technologies team describes prompts as the conversation starters you use to obtain specific results. Prompts can be a phrase, a few sentences or a longer set of instructions. Advanced models accept multimodal inputs such as images and audio.

Think of a generative AI tool like ChatGPT as a machine you are programming with words. Your results depend heavily on how you frame the prompt: clear, specific instructions produce more relevant answers, while vague requests lead to generic responses.

3. Prompt engineering basics

Prompt engineering is the art of crafting prompts to guide AI models toward useful outputs. Effective prompt engineering involves:

• Providing context – give the model background information, define roles or set a persona.
  For example, “You are an experienced wildlife biologist…” yields a different answer than a simple question.
• Being specific – include details, examples, constraints or desired formats. Specific queries, such as “Discuss the economic implications of climate change in developing countries over the next decade,” produce more targeted responses.
• Building on the conversation – chatbots remember the context of previous messages. You can refine results by asking follow‑up questions or instructing the AI to adjust tone without repeating all the context.

The MIT guide identifies several common prompt types:

• Zero‑shot prompts: simple, clear instructions with no examples (e.g., “Summarize this article in five bullet points”).
• Few‑shot prompts: include a few examples to demonstrate the desired style or structure.
• Instructional prompts: use verbs like “write,” “explain” or “compare” to direct the AI.
• Role‑based prompts: ask the AI to adopt a specific persona (e.g., a marketing expert or historian).
• Contextual prompts: supply background information or target a specific audience.

Prompt engineering is an iterative process:

1. start with a prompt
2. evaluate the output
3. and then refine your instructions.

These are OpenAI’s best‑practice guide notes that, clear specific prompts and iterative refinement are key to obtaining accurate results.

4. Advanced prompting strategies

OpenAI’s GPT best practices outline several techniques to improve results:

• Write clear instructions: include details, adopt a persona if relevant, and specify the steps you want the model to perform.
• Provide reference text: copy relevant excerpts or data into the prompt to ground the model’s response and reduce hallucinations.
• Split complex tasks into smaller tasks: ask the model to break down a complex assignment into simpler parts.
• Ask the model to show its reasoning: requesting a chain of thought can produce more reliable answers.
• Use external tools: supplement the model with external sources (e.g., retrieval systems or calculators) to improve accuracy.
• Test systematically: tweak prompts and compare results across multiple examples to ensure improvements aren’t isolated.

5. Why prompt engineering matters

5.1 Improve AI outputs

Prompt engineering directly influences the quality of AI outputs. Skillsoft’s 2025 report notes that prompt engineering involves crafting effective inputs that guide models like ChatGPT to produce accurate, relevant and useful outputs.
Asking “Summarize this article in two bullet points for a business audience” yields better results than simply saying “Summarize this”.

5.2 Unlock diverse applications

Understanding how to prompt effectively unlocks many capabilities, including:

• Content generation: writing blog posts, social media updates or product descriptions.
• Customer support: answering FAQs and troubleshooting issues.
• Personalized recommendations: suggesting products or content based on user preferences.
• Translation and localization: bridging language barriers.
• Drafting and editing: assisting with professional correspondence or academic writing.

These are some of the recommended use cases for ChatGPT identified by marketing analytics platform Hurree.

5.3 Career opportunities

Prompt engineering is now recognised as a valuable skill. The Skillsoft report lists prompt engineering as one of the top in‑demand AI skills and notes that mastering it enables people to leverage AI tools, solve problems faster and create better outcomes with less effort.
A separate Deloitte survey found that 94 % of executives believe investment in AI will be critical to business success over the next five years, yet 57% of tech leaders say their teams’ AI skills are low.
Learning prompt engineering helps close this skills gap and improves job security by making workers adaptable in an AI‑driven economy.

5.4 Broad relevance

AI literacy isn’t just for programmers. Skillsoft’s FAQ explains that while not every employee needs to be an AI expert, a basic understanding of AI and prompt engineering benefits roles across marketing, HR, sales and operations.
It empowers individuals to automate routine tasks, generate data‑driven insights and collaborate more effectively.

6. Responsible use and limitations

6.1 Know the limitations

Generative models occasionally produce incorrect or fabricated information. According to the TruthfulQA benchmark cited by Hurree, current generative AI models are completely truthful only about 25 % of the time. This means you should always verify facts from trusted sources before acting on them.

ChatGPT also struggles with nuanced tasks requiring empathy or emotional intelligence and is not suitable for situations demanding complete data accuracy.

6.2 Ethical considerations

AI can replicate biases present in its training data. When using chatbots in sensitive contexts (e.g., hiring or lending decisions), ensure there is human oversight and adhere to legal and ethical guidelines. Be transparent about the use of AI, avoid generating harmful content and respect user privacy.

7. Getting started: using ChatGPT

If you’re new to ChatGPT, follow these steps (summarized from Zapier’s August 20 2025 guide):

1. Create an account: Visit Chat GPT or download the mobile/desktop app and sign up using your email, Google or Microsoft account.
2. Choose a model: Paid subscribers can select from different AI models (e.g., GPT‑4o or GPT‑4.5) via a dropdown menu.
3. Enter your prompt: Type your query, speak it using voice mode, or upload images/files.
   You can also ask ChatGPT to search the web or conduct deep research by clicking the “+” icon and selecting the relevant option.
4. Review and refine: After you receive a response, you can edit the prompt, ask follow‑up questions, regenerate the answer, review sources or copy the output.

Spending time refining your prompts and providing context will result in more useful and accurate outputs.

8. Practice and further learning

To develop your prompt engineering skills:

• Experiment: Use different prompt types and observe how the model’s responses change. Ask follow‑up questions and refine your prompts.
• Train the model: In a single chat thread, ChatGPT remembers information you share. Providing background about your business or project helps it tailor future answers.
• Ask for clarification: Encourage ChatGPT to ask you questions before it begins a task; this leads to more accurate outputs.
• Stay current: AI tools evolve rapidly. Follow reliable sources—blogs, research papers, industry reports—and enrol in courses to keep your skills up to date.

Conclusion

AI and chatbots are rapidly reshaping the world of work. Learning the fundamentals of AI and ML, practising prompt engineering and understanding how to use chatbots effectively will help you thrive in the future.

Clear, context‑rich prompts enable AI models to produce better outputs, while skills in prompt engineering and AI literacy are increasingly sought after by employers and coupled with responsible use and a willingness to learn, these skills can empower you to automate tasks, unlock creativity and stay competitive in an AI‑driven economy.