We are all used to using many search engines, with Google being one of the most popular. While doing our searches over the years, we have understood that “how you search” is key to getting results in a way you expect them to be provided. Essentially you need to know how to “prompt”
Now, with the advancement of several AI tools and Large Language Models (LLMs) underlying, there is an emerging concept called “Prompt Engineering.”
What is Prompt Engineering?
Prompt engineering is essentially the art and science of crafting effective prompts to get the desired output from large language models (LLMs) like me. Think of it as having a conversation where the clarity and nuance of your questions (prompts) directly influence the quality and relevance of my responses.
Instead of just asking "Tell me about the history of the internet," a prompt engineer might ask something more specific, like: "Explain the key technological innovations that led to the development of the World Wide Web between 1989 and 1995, focusing on the roles of Tim Berners-Lee and the CERN research institute. Please provide your answer in three concise paragraphs."
As you can see, the second prompt provides much more context and guidance, leading to a more focused and informative answer. Prompt engineering involves understanding how LLMs interpret language, experimenting with different phrasing and structures, and iteratively refining prompts to achieve specific goals. It's a crucial skill for unlocking the full potential of these powerful AI tools.
How is prompt engineering different from what we did using search engines?
While there's some overlap in the sense that both involve formulating queries to get information, prompt engineering for large language models (LLMs) is fundamentally different from using search engines in several key ways:
1. Goal and Output:
Search Engines: The primary goal is information retrieval. You input keywords or phrases, and the search engine returns a list of relevant web pages or documents that already exist. You then need to sift through these results to find the specific information you're looking for.
Large Language Models (via Prompt Engineering): The primary goal is content generation and task completion. You provide instructions or questions (prompts), and the LLM generates new text, code, or other outputs based on its training data and understanding of your prompt. It doesn't just retrieve existing information; it synthesizes and creates.
2. Input Style:
Search Engines: Historically, effective search often relied on using specific keywords and Boolean operators (AND, OR, NOT) to narrow down results. While natural language search has improved, concise keywords are still often the most effective approach.
Large Language Models: Prompt engineering thrives on natural language and complete sentences. You can provide context, specify the desired format, ask for explanations, and even instruct the model to adopt a specific persona. The more natural and detailed your prompt, the better the LLM can understand your intent.
3. Interaction Paradigm:
Search Engines: The interaction is typically a single query followed by a list of independent results. Subsequent searches are usually new, isolated queries.
Large Language Models: Prompt engineering often involves a conversational or iterative approach. You can build upon previous turns, refine your requests, and ask follow-up questions, leveraging the model's memory of the ongoing interaction.
4. Understanding and Context:
Search Engines: Primarily work by matching keywords to content on web pages. While they understand some semantic relationships, their understanding of context and nuance is limited.
Large Language Models: Are trained on vast amounts of text data, enabling them to understand complex relationships, context, and even implicit meanings in your prompts. They can reason, infer, and generate more nuanced and contextually relevant responses.
5. "Truth" and Factuality:
Search Engines: Aim to provide links to what exists on the web, without necessarily evaluating the truthfulness or accuracy of the content. The user is responsible for evaluating the sources.
Large Language Models: Generate responses based on patterns learned from their training data. While they can often provide factual information, they are also prone to "hallucinations" or generating incorrect information that sounds plausible. Prompt engineering can help mitigate this by asking for sources or specific reasoning, but critical evaluation of the output is still necessary.
Here's an analogy:
Search Engine: Imagine a vast library with a very efficient catalog system (the search engine). You provide keywords (like "history of wearable devices"), and the catalog gives you a list of relevant books (web pages). You then need to go and read the books to find the specific information you need.
Large Language Model (with Prompt Engineering): Imagine having a knowledgeable research assistant who has read a vast library. You can ask this assistant a detailed question ("Explain the evolution of health monitoring features in wearable devices over the last decade, including key technological advancements and their impact on user behavior"). The assistant will then synthesize information from its knowledge and generate a comprehensive answer for you.
In essence, using a search engine is like finding existing pieces of a puzzle, while prompt engineering with an LLM is like asking a skilled puzzle solver to create a new picture based on your instructions.
Now we have gotten some basic idea of what we did through search engines and how we can go about using LLM’s through effective prompts.
How does one learn about prompt engineering?
Learning about prompt engineering is an ongoing process, as the field is rapidly evolving with new models and techniques emerging frequently. However, here's a breakdown of how you can get started and continue to learn:
1. Understand the Fundamentals:
What are Large Language Models (LLMs)? Begin by grasping the basic concepts of how LLMs work, their capabilities, and their limitations. You don't need a deep technical understanding, but knowing the basics of how they generate text is helpful.
Core Prompting Concepts: Familiarize yourself with fundamental prompting techniques like providing clear instructions, specifying the desired format, using examples (few-shot prompting), and defining the role the AI should adopt.
Key Components of a Prompt: Learn about the different parts of a prompt, such as instructions, context, input data, and desired output format.
2. Explore Online Resources:
Prompt Engineering Guides: Numerous free guides are available online that offer structured learning paths and practical advice. Some popular ones include:
Prompt Engineering Guide (DAIR.AI): A comprehensive resource covering various techniques and concepts.
Learn Prompting: A free and open-source guide with beginner to advanced modules.
Anthropic's Prompt Engineering Overview: Focuses on their Claude model but contains broadly applicable principles.
Google Cloud's Prompt Engineering Guide: A good starting point for understanding the basics.
Online Courses: Platforms like Coursera, Udemy, and edX offer courses specifically focused on prompt engineering. Some popular options include:
Prompt Engineering for ChatGPT (Vanderbilt University on Coursera)
Google Prompting Essentials (Google on Coursera)
ChatGPT Prompt Engineering for Developers (DeepLearning.AI)
The Complete Prompt Engineering for AI Bootcamp (Udemy)
OpenAI Documentation and Playground: Explore OpenAI's official documentation for their models (like GPT) and experiment directly with the OpenAI Playground to test different prompts and observe the results.
Research Papers: For a deeper understanding, you can explore research papers related to prompting techniques and LLM behavior.
GitHub Repositories: Many community-driven "Awesome Lists" on GitHub curate valuable prompt engineering resources, examples, and tools.
3. Practice and Experiment:
Hands-on Experimentation: The best way to learn is by doing. Experiment with different prompts on various LLMs for different tasks. Observe how subtle changes in wording can significantly impact the output.
Analyze Examples: Study well-crafted prompts and their corresponding outputs to understand what makes them effective. Many online resources and communities share example prompts.
Iterative Refinement: Prompt engineering is often an iterative process. Don't expect to get the perfect output on your first try. Analyze the results, identify areas for improvement, and refine your prompts accordingly.
4. Engage with the Community:
Online Forums and Communities: Platforms like Reddit's r/PromptEngineering, the OpenAI Developer Forum, and Discord communities dedicated to specific models are excellent places to ask questions, share insights, and learn from others.
Follow Experts: Keep up with researchers and practitioners in the field by following them on social media and reading their blog posts.
5. Stay Updated:
Follow the Latest Developments: The field of LLMs and prompt engineering is constantly evolving. Stay informed about new models, techniques, and best practices by reading blog posts, research papers, and attending webinars or conferences.
Key Skills to Develop:
Clear and Concise Communication: The ability to articulate your requests effectively is crucial.
Analytical Thinking: You need to analyze the output of LLMs and identify areas for improvement in your prompts.
Creativity and Experimentation: Trying different approaches and thinking outside the box can lead to better results.
Problem-Solving: Prompt engineering often involves troubleshooting and finding the right way to get the desired outcome.
(Optional but helpful) Basic Programming Skills: Familiarity with Python can be beneficial if you want to interact with LLM APIs or build applications using them.
(Optional but helpful) Understanding of NLP Concepts: Basic knowledge of natural language processing can provide a deeper understanding of how LLMs process text.
By combining these learning methods and actively experimenting, you can develop strong prompt engineering skills and effectively harness the power of large language models. Good luck on your learning journey!
Why don’t we try to use ChatGPT to prompt it to develop and give us what we are looking for? Let me first ask ChatGPT about the prompt engineering example, and then take “wearable device” and break it out
❓Prompt: Walk me through a prompt engineering example
✅ChatGPT response: Great! Let's walk through a live prompt engineering example based on something you’re interested in.
First, please tell me what you'd like the AI to help with. Some ideas to choose from (or suggest your own):
Writing: a blog post, poem, email, or story
Coding: debug or generate a script
Planning: a trip, project, event, or schedule
Learning: a concept, tutorial, or explanation
Creativity: generate names, slogans, taglines
Decision-making: pros/cons, prioritizing tasks, etc.
What would you like to focus on for this prompt experiment?
❓Prompt: Focus on wearable devices
✅ChatGPT response: Perfect—let’s explore prompt engineering for wearable devices. We’ll take a topic and refine prompts step-by-step to show how better prompting improves output.
🎯 Goal:
Let’s say you want a summary of the latest trends in wearable tech for health tracking in 2025.
🧪 Step-by-Step Prompt Refinement:
1. Basic Prompt (Too Vague):
"Tell me about wearable devices."
Result: Generic overview (e.g., mentions smartwatches and fitness trackers without depth or current relevance).
2. Refined Prompt (More Specific):
"What are the current trends in wearable devices in 2025, especially those focused on health and wellness?"
Improved Result: Includes topics like continuous glucose monitoring, stress detection, smart rings, etc.
3. Engineered Prompt (Structured, Contextual):
"You are a technology analyst writing a 3-paragraph summary for a health startup founder. Summarize the top 3 trends in wearable technology in 2025 focused on health tracking, including innovations in sensors, AI integration, and user adoption."
Result:
Tailored to a specific reader (startup founder)
Organized content
Deeper insight on sensors, AI, and adoption patterns
4. Even Better (Add Format):
"Write a bulleted briefing for a health startup founder summarizing the top 3 trends in wearable devices for health tracking in 2025. Focus on sensor innovation, AI use, and adoption challenges. Limit each trend to 3 concise bullet points."
Result:
Clean, easy to skim
Actionable insights
Structured for a business use case
Would you like to try this prompt yourself in ChatGPT or want me to generate a sample response using the final version?
❓Prompt: Please generate a sample response using the final version
✅ChatGPT response: Sure! Here's a sample response using the final, engineered prompt:
Briefing for Health Startup Founder: Top 3 Trends in Wearable Devices for Health Tracking (2025)
1. Advanced Sensor Innovation
Non-invasive continuous glucose monitoring is now viable in commercial smartwatches and smart rings.
Sweat-based sensors are gaining ground for hydration, electrolyte, and cortisol tracking.
Multi-modal sensors (ECG, SpO2, skin temperature) are being integrated into ultra-thin form factors.
2. AI-Driven Personalization
On-device AI enables real-time analysis of vitals, improving responsiveness without cloud lag.
Machine learning models adapt to individual baselines, reducing false positives in health alerts.
Predictive health coaching features use longitudinal data to suggest lifestyle changes proactively.
3. Adoption & Market Shifts
Health insurers and employers are subsidizing wearables to drive engagement and reduce claims.
Older demographics are embracing wearables due to improved UX and medical-grade features.
Privacy concerns are driving demand for local data processing and transparent AI models.
It also generated a blog post and a cover image (shown below)
Did the same with my other friends Gemini, Copilot and the results were amazing as you refine what you ask for and with a focus. LLM’s generate what you are looking for “blog post”, “writeup”, “article”, “marketing slide” etc, but they key to get it to create the content lies in effective Prompt Engineering
References: