When I first came across the term, ‘prompt engineering’ and knew what it meant for a prompt engineer to maximize Generative AI models like ChatGPT, I was bewitched by its appeal.
And why not?
It felt like a key to unlocking the full potential of a super-intelligent AI model, which unfortunately feels like a dreadful alien concept to most skeptical minds around the globe today.
It amazed me how some creatively worded instructions can elicit some of the most unimaginable yet sound responses from a Gen AI model.
That’s the prompt engineering, a skill to let your generative AI system do the talking in response to your questions.
Why There Is A Need For Prompt Engineering?
In contrast to what many people believe, AI machines are still not intelligent enough to intuitively understand your questions. Most of the time, you may have noticed Gemini or ChatGPT weirdly behaving while answering your questions.
Either they switch to fabricating the answer or regurgitate the same facts. This is where we realize a very important thing about the AI model, that it is neither psychic nor telepathic.
It doesn’t know a user’s true intention until it is clearly conveyed.
AI understands the user’s intention better when you give it some semantically worded or precise parameters for your question. If you fail to convince an AI model to generate a desired output, you won’t be able to maximize its true potential.
This is where prompt engineering comes into the picture.
It enables you to prompt the AI as efficiently and effectively as possible. Understandably, this importance of prompt engineering has given rise to the demands of prompt engineers as a viable career path.
What is Prompt Engineering?
Prompt engineering is all about getting into the thinking patterns of an AI model by prompting it to respond to your apt and creatively worded questions (or prompts).
So, a person skilled in eliciting the best response from a generative model against the given prompts is a prompt engineer.
For a prompt engineer, it is important to know that a generative AI model will behave according to the nature of the questions it is asked.
Hence, mind your questions, as the model’s response is limited to what you ask.
So, prompt engineering teaches you the art of understanding HOW TO ASK to get the most meaningful answer from an AI model. This certainly involves a good command of your language skills to use grammar and vocabulary arts to ask creatively worded queries to an AI model.
An Exemplified Definition Of Prompt Engineering
The definition of Prompt engineering can be narrowed down to maneuvering a Gen AI model, just like a driver maneuvering a car, practically and intuitively.
You see, Gen AI models don’t do anything on their own. They rely on your prompts. And the way you adjust (maneuver) your prompts (or input data) in real-time and based on your (intuitively felt) experience with the model, you successfully manage to generate the best outputs from the model.
To say otherwise, as a prompt engineer, you gradually learn how the model behaves in response to your input data. You get this feeling over time, just as a driver has the feeling (or intuition) of driving different terrains until he becomes skilled at maneuvering the vehicle at will.
What Involves In Prompt Engineering?
A prompt engineer has a specific skill set that makes him different from any common generative AI user who just uses the AI model as an answering machine. A skilled prompt engineer goes deeper into the nuances of language input and carefully observes how the AI system generates the responses.
Based on the response pattern of the AI system, the engineer gains an understanding of how to refine the development of LLMs (large language models). It also helps the engineer find AI limitations, errors, or defects that AI programmers can address.
The efficiency of a prompt engineer doesn’t end here. He also lends his expertise to training the AI to understand and interpret various prompts. A prompt engineer’s role is a mixture of programming, instructing, and teaching.
Difference Between An Effective And An Ineffective Prompt
Based on the aforementioned information about prompt engineering, there is no doubt that it is an art of eliciting the best and most appropriate response from a generative AI model like ChatGPT against the relevant prompt given to it.
So, “prompt” is what lays the foundation for coaxing an AI system into giving out the apt response. Therefore, it is important to distinguish between an effective and ineffective prompt.
Example Of An Ineffective Prompt:
“Who won the race in the Olympics?”
The above prompt is ineffective because it doesn’t provide exact context. It is far too broad. There is a strong likelihood that the ChatGPT will return the answer by enumerating every winner of every race ever held in the Olympics based on its access to the available data regarding this.
Obviously, a user will have to face an endless load of information, too difficult to manually parse.
Example Of Effective Prompts:
“Who won the men’s 100-meter race at the 2024 Paris Olympics?
The above is an example of an effective prompt because it provides more details in the prompt. This helps the AI understand the context better and accordingly respond to the prompt with relevant information.
What Does A Prompt Engineer Do?
If talking from the standpoint of employment, a prompt engineer is similar to a UI engineer. He ensures that a UI is intuitively designed, easily navigational, and trouble-free for users.
In the context of Gen AI, a prompt engineer must know the working mechanism of AI. He must know how the AI model responds against specific input prompts and how to elicit most appropriate and relevant results from it.
The Multi-Faceted Roles Performed By A Prompt Engineer
Handling Development, Testing, And Refinement of AI Prompts
A prompt engineer directly works with AI platforms to develop new prompts, test how the AI model behaves, and give output in response to the prompts. The engineer improves prompts or implements protocols (or AI guardrails) on the AI model to ensure that the system is safe from generating biased or harmful content.
He ensures that the model’s behavior is more predictable and controllable, or whether its generated outputs align with human values and societal norms.
By the way, this implementation of AI guardrails is a collaborative and multi-pronged approach. Why? Because a prompt engineer is part of a large ecosystem of developers, data scientists, policymakers, and society.
Hence, an engineer can’t enforce changes to the AI model on the spur of the moment or based on his hunch or discretion.
Serving As A Quality Controller In Cross-Disciplinary Teams
Developing prompts for an AI system is not a lone practice of a single prompt engineer. In this case, the engineer works with a development team in designing and coding the AI platform. He also works with a data team that trains an AI model using a large dataset.
When working for a company, the prompt engineer is to perform in alignment with his company’s goals and user expectations.
Analyzing Prompt Data Sets To Measure Performance Metrics Of The AI System
Nobody can be a successful prompt engineer if they don’t understand analytics. That said, the engineer must supervise, and correlate data inputs and outputs. Doing so leads to measuring the behavioral and performance metrics of an AI system. These analytics of a prompt engineer finally helps developers, data scientists, and business teams.
How Much Salaries Do Prompt Engineers Earn?
As reported, the average salary of prompt engineers in India ranges from INR 17.6 lakhs to INR 172.0 lakhs. However, the extent of salary earned by a prompt engineer depends on their job complexity and responsibilities within the prompt engineering field.
Prompt Engineering Roadmap: Skills You Must Have To Become An Efficient Prompt Engineer
Efficiency In Programming
Though not mandatory, but you can’t rule out the involvement of coding in prompt engineering. Whether you work with a development team to build an AI platform or are tasked to automate testing and other functions, it is necessary to have knowledge and experience with Python, APIs, operating systems, etc. Now, the extent of requirements for prompt engineering depends on your assigned job responsibilities.
Verbal And Written Communication Excellence
As a prompt engineer, you should be efficient in interacting with an AI system through creative words and phrases. Well-crafted commands induce a better understanding of how an AI system responds to the data inputs. Also, verbal and written communication skills are very important, given the cross-disciplinary nature of the prompt engineering role.
Previous Experience In Prompt Engineering
A basic understanding of prompt engineering is necessary. Most companies prefer prompt engineers with demonstrated skills in developing and testing AI prompts. You will also increase your chance for employability with your proven experience of working with major Generative AI models, like ChatGPT.
Knowledge In AI Technology
As a prompt engineer, you must have a comprehensive knowledge of LLMs (large language models), NLP (natural language processing), and machine learning. You should also know how AI-generated content is developed.
Experience In Data Analysis
To become an efficient prompt engineer also involves understanding how data works in the context of prompt engineering. The engineer should know data analytics techniques and tools. This comes in handy for you in employment where you have to analyze structured and unstructured data sources and spot data bias.
In addition, you can also pursue formal education in BS in Computer Science, Engineering, or a related field to lend more gravity to the significance of your skills as a prompt engineer. You must be a problem-solver with good analytical skills.
List Of Glossaries Used In The Prompt Engineering World
With prompt engineering catching up fast, here’s the list of popular slang and terms used in the field.
- Token – It is a piece of text (i.e. a character, word, or a part of a word). It is processed individually by LLMs. Token is limited to every language model to handle data input and output responses.
- Prompt chaining – A step-by-step technique to use a sequence of prompts. Each prompt builds on the former one. It helps the AI model go through a structured process to attain a more refined answer.
- Temperature – It is a parameter or a measurable factor in LLMs (e.g. ChatGPT). Temperature determines the creativity or randomness of the model’s output. Lower temperatures (e.g., 0.2) make the model more focused and deterministic. That’s why it is suitable for tasks requiring accuracy and reliability. Higher temperatures (e.g., 0.8 or 1.0) determine creativity or randomness, enabling the model to pick less probable but diverse responses. Best for tasks requiring imaginative outputs like storytelling or poem writing. There is also a medium temperature (e.g. 0.5) that balances between creative and logical responses.
- Zero-Shot / Few-Shot Prompting – In this technique, Gen AI models are given information before they are asked to complete a task. In zero-shot prompting, the AI model is asked to complete a task without giving it any specific background information or examples. In few-shot prompting, you provide some examples, contexts, or information to the AI model. Then it generates a response according to the input data.
- Prompt leakage – A situation when an AI model is asked or forced to spill over its own prompt. It happens mainly due to too much information is included in the prompt.
- Hallucination – AI-generated response that is factually incorrect, though sounds plausible.
- In-context learning – The ability of an AI model to learn patterns from examples within a prompt without needing additional training.
- Context window – The max amount of text (including both the prompt and its response) an AI model considers at one time.
- Nucleus Sampling (Top-p Sampling) – A text-generation technique to help an AI model to generate diverse and quality outputs.
- Reinforcement Learning from Human Feedback (RLHF) – A machine learning technique to help ML models self-learn more efficiently based on human feedback.
Prompt Engineering FAQs: Exploring The Working Mechanisms Of Prompt-Driven AI Models
How can someone without a technical background learn prompt engineering, and be good at it?
- Learn the core concepts of language models like “token,” “temperature,” “zero-shot/few-shot prompting,” and “context window.”
- Read resources like documentation, research papers, journals, or blog posts on NLP topics
- Experiment with different types of prompts on any Gen AI platform, like Gemini or ChatGPT. For example, use short and long prompts, questions, and context and observe how different prompts generate specific responses.
- Try prompt chaining methods to direct the model to give you multi-part responses.
- Learn different types of prompt design techniques such as few-shot prompting. They help the model structure answers according to your desire.
- Go for online resources, courses, or communities (e.g. GitHub) where prompts are refined for quality outputs.
Are clear-cut and refined prompts necessary?
Yes, they are necessary for the model to understand your instructions better and generate quality answers accordingly.
What about the AI model’s ‘receptivity’ to understand prompts clearly? Most of the time, the model falls back to repetition.
Yes, the model’s receptivity surely impacts the quality of responses. However, the ability of a model to interpret nuanced instructions (i.e. receptivity) is subject to several factors. One of them relates to the quality of intense training it has gone through. The model’s ability to understand your prompts depends on statistical patterns, not accurate comprehension. That explains why the model reverts to generic or repetitive responses, even if you give it a well-crafted prompt.
Should I learn programming language before learning prompt engineering?
Though programming language is useful to handle AI-related tasks, like building AI applications, it’s NOT mandatory. Prompt engineering generally denotes your ability to interact with an AI model, enabling it to generate useful and relevant outputs.
When we talk about a ‘prompt’ in the context of Generative AI models, what does it mean exactly? Is it just input data, questions, or context?
In generative models like Gemini or ChatGPT, the “prompt’ signifies input data so that the model can generate a response. Hence, the prompt could mean anything, from input text to context, and task instructions. In fact, the prompt is not confined to just input data. It’s any piece of info, context, or guidance to elicit relevant or context-specific responses from the model.
Is there any word limit when prompting a Gen AI model?
No. there is no word limit on prompts unless you know how much context or details you provide to the model to get the desired output from it. Avoid long-winded or overly complex prompts. They may confuse the model resulting in unexpected or irrelevant responses.
Can I include a combination of questions, contexts, and examples in prompts?
Yes, and doing so would improve the model’s efficiency in generating relevant and tailored responses.
Conclusion
With intelligent machines influencing nearly every aspect of human life today, the need to understand prompt engineering has become more conspicuous than ever.
You can’t avoid it, especially when you know very well that the pace of AI innovations is just relentless. Therefore, understanding the working mechanisms of prompt-driven AI models through prompt engineering is vital for your knowledge and career.