Prompt engineering: A guide to AI prompting for beginners

What is prompt engineering?

Prompt engineering is the process of guiding generative AI tools, particularly LLMs (large language models), to produce the desired outputs. By carefully crafting AI prompts—or the instructions you give to AI—you provide the model with context, examples, and detailed directions that help it respond in a meaningful way.

Think of it like writing a recipe for the AI. Like a chef, you're providing the instructions and the ingredients needed to create the specific output you have in mind. The goal of prompt engineering is to get generative AI to work as intended, and reliably produce useful and contextually relevant outputs. 

What is an AI prompt?

In the context of AI, a prompt is the input you provide to the model to generate a specific response. In other words, it’s the set of instructions you give to a generative model to guide its output.

These instructions can take various forms, ranging from a simple question to multi-step instructions to code snippets. They can also include multimodal inputs such as pictures and audio. 

Ultimately, the quality and relevance of the AI’s output is directly influenced by the effectiveness of the AI prompt.

Key components of effective prompt engineering

Prompt engineering is an iterative process that guides and fine tunes model behavior by creating prompts that are clear, concise, and consistent.

The challenge is: AI models are as open-ended as they are powerful. They can perform various tasks like summarize texts, write code, and answer questions, all from as little as a single keyword input. 

AI models work by predicting the next best output based on the prompt given. However, because they have no innate sense of context, they rely heavily on the subtleties of human language to direct their output.

For this reason, it can help to think of prompt engineering as ‘programming a machine with words’ to get back a certain response. As a result, the key to effective prompt engineering is using clear language and providing lots of detail and context.

This typically involves the following elements:

Instruction: The core of the prompt, the instruction provides an understanding of the task at hand, helping the model to frame its output appropriately. For example, “Summarize this text” provides a clear action to take. The more clear, concise, and specific the instructions, the more likely the LLM is produce a response that meets your exact requirements. 

Prompt format: The structure of your prompt plays a key role in how the model interprets and responds to your input. Prompt formats can range from simple questions in natural language to direct commands to structured inputs with specific fields.

Task context: Context provides additional information that helps the model understand the broader scenario or background. For instance, "Considering the rise of AI, create a business plan" gives the model a backdrop against which to frame its response.

Examples: Examples greatly improve the model’s understanding and performance on a task, as they serve as concrete illustrations of the expected output. For instance, you can drop in text from an existing piece of content you want to emulate to guide output and improve results.

Input data: This includes any specific information or data you want the model to process for a given task. It could be a whole book, a set of numbers, or a single phrase.

Multi-turn conversations: Designing prompts for multi-turn conversations allows users to engage in continuous and context-aware interactions with the AI model, enhancing the overall user experience.

Output indicator: This element provides the model with a known format or type for its output. For example, "In the style of J.K. Rowling, rewrite the following sentence" gives the model a stylistic direction, which can be especially helpful in roleplaying scenarios.

Fine tuning: Fine-tuning the AI model on specific tasks or domains using tailored prompts can enhance its performance. Additionally, adapting prompts based on user feedback or model outputs can further improve the model's responses over time.

Key prompt engineering techniques: Types of AI prompts

You can craft prompts for AI in various ways, depending on the desired output. The most common of these prompt engineering techniques include:

Direct prompts (Zero-shot)

Zero-shot prompting provides the model with a direct instruction or question without any examples (aka shots). This is useful for broad, open-ended queries, such as when you ask the model to summarize a text, translate a text, or generate new ideas or brainstorm solutions. 

Example prompt: “Explain what a large language model is.”

One-, few-, and multi-shot prompts

This method provides one of more examples (shots) of the desired input-output pairs before presenting the actual prompt. This context helps the model to better understand the task and generate more accurate responses.

Example one-shot prompt: “Basketball is a game of dribbling and shooting. Explain why it's popular." Few-shot: "Basketball involves dribbling and shooting, and soccer requires kicking and passing. What skills are important for tennis?"

Chain of Thought (CoT) prompts

This involves asking the model to break complex reasoning down into a series of steps. By guiding the model from one step to the next, this can lead to a more comprehensive, useful, and well-structured output. 

Example prompt: “Describe the process of building an AI agent, from start to finish.”

Zero-shot CoT prompts

This method guides a model through a series of steps without examples, aiming to leverage the model's inherent knowledge. It can involve adding a phrase like "Let's think step by step" to the prompt, which invites the model to articulate its reasoning and can help to produce a better output.

Example prompt: "Is 523 divisible by 3? Explain your reasoning."

Related reading:

• 18 prompt engineering techniques explained

• Microsoft: Prompt engineering techniques

Prompt engineering examples and use cases

AI models are powerful tools with various applications. Whether you're looking to generate content, analyze documents, answer questions, create visuals, write code—a well-crafted prompt is how you shape the output's style, structure, and content to your needs.

Here are some key examples and use cases for prompt engineering:

Language and text generation

Question answering

Code generation

Image generation

Prompt engineering best practices

Prompt engineering relies on providing specific instructions to get specific outputs. By providing the model with a set of meaningful constraints, you can get it to think and create in alignment with your wishes.

You can follow these prompt engineering best practices to quickly develop effective prompts:

1. Set clear goals and objectives

2. Provide context and background information

3. Use few-shot prompting (include examples)

4. Be specific

5. Iterate and experiment

6. Leverage chain of thought prompting

7. Use cues to guide tone, role, or format

Dive deeper: Google: 5 best practices for prompt engineering

What are the benefits of prompt engineering?

Done well, prompt engineering enables you to harness the vast potential of generative AI models for any number of specific applications. 

With AI becoming more embedded in our personal and professional lives, prompt engineering is shaping up to be a highly valuable skill that not only improves personal efficiency, but also leads to better business outcomes.

Other key benefits of prompt engineering include:

Improved model performance: Prompt engineering can significantly enhance the accuracy and relevance of AI outputs.

Reduced bias and risk: By clearly defining perspectives or contextual requirements, well-designed prompts can help mitigate inherent biases in AI model’s training data.

Greater control and predictability: By standardizing prompt formats and using system-level instructions, businesses can encourage consistent responses, helping to mitigate risk and ensure AI compliance.

Enhanced user experience (UX): Good prompts lead to more intuitive, helpful, user-friendly AI interactions that help to reduce misunderstandings and aligning outputs with user expectations.

11 Prompt engineering tips

Prompt engineering is an evolving field that’s both an art and a science—requiring a mix of creativity and precision to get desired outputs. As a science, it involves methodical experimentation, logical structuring, and data-driven tweaking to reliably produce quality outputs. As an art, it requires intuition, linguistic finesse, and an understanding of human psychology to craft prompts that elicit the most useful and relevant responses.

These prompt engineering tips can help to guide your thinking:

1. Prompt like you're talking with a person

Include the level of detail and context you'd include when explaining a problem to a child, rather than just using keywords like when searching online with Google.

Example: Instead of just entering the term "AI agent," ask a complete question: "How can you use an AI agent?"

2. Assign a role

By asking the model to behave as a specific persona, you get outputs that better align with the expertise and perspective of that field. 

Example: "You are a corporate lawyer. Evaluate this AI governance strategy in context of the current regulations on AI compliance.”

3. Break complex tasks into steps

LLMs often perform better if the prompt is broken down into smaller steps, as it helps them to think through each step in sequence rather than all at once.

Example: Create a multi-step prompt such as: “Step 1: Extract 3–5 factual claims from the text below. Step 2: For each extracted claim, write a search query that could be used to verify it using reliable sources.”

4. Balance openness and specificity

Models need goals and context, but there’s value to leaving prompts open-ended, especially in creative fields. Models can produce novel insights, ideas, and content if their word choice, tone, and structure aren’t too tightly defined.

Example: Try something like “Create a catchy slogan for an eco-friendly water bottle. The message should appeal to environmentally conscious young adults and highlight sustainability. Use your judgment to make it memorable.”

5. Use clear syntax

Using clear syntax in your prompt—such as punctuation, headings, and section markers—helps communicate intent and make outputs easier for the model to parse, especially in multi-step prompts, leading to more structured, accurate outputs.

Example: Try using syntax like: "STEP 1: Summarize the text below. --- STEP 2: Generate three search queries based on your summary. ---" The --- acts as a clear stopping point between steps, and uppercase headings help the model distinguish phases of the task.

6. Repeat instructions at the end

Models can be susceptible to recency bias, meaning information at the end of the prompt can have more influence over the output than information at the beginning. Repeating instructions at the end of the prompt can help ensure a more relevant, consistent output.

Example: Instead of ending a prompt abruptly, bookend it with the instructions you started with to help the model stay on task from start to finish.

7. Give the model an “out”

AI models can hallucinate, or confidently generate false outputs, when they don't have enough information. To help prevent this, build a fallback option into prompts that tell the model what to do if it can’t confidently complete the task.

Example: For reviewing a text, you could include phrases such as “If the content is accurate, evaluate, summarize, and include” or “If inaccurate, omit but include footnote as to why.”

8. Try conditional execution

This involves telling the model to perform an action only if specific criteria are met. Akin to "if-then" logic, it lets the AI skip, adapt, or add steps as needed. By controlling model behavior based on input, it helps prevent hallucinations and supports multi-step workflows where every step isn’t always needed.

Example: For an AI agent for customer service, a prompt could say: “If the user mentions a technical error, initiate a step-by-step troubleshooting workflow. If it's a general question, provide a brief explanation.”

9. Take an iterative approach

It can help to begin with a broad prompt and gradually refine it based on the model’s responses. Known as prompt chaining, this adds constraints one by one, even using the model’s outputs as part of a feedback loop to inform adjustments, which can later be combined into one long prompt.

Example: Start with a basic prompt like “Describe how AI can improve customer service,” and based on output, refine it further—e.g., “Now focus on enterprise companies and include examples of key tools.”

10. Ask AI

AI models are knowledgable on many subjects, including prompt engineering. To identify areas on improvement, ask the model to analyze your prompt, or provide a prompt templates or a meta prompt to guide your efforts.

Example: Ask: “How would you rewrite this prompt to be more effective?” or “What additional context would improve this prompt?”

11. Think like a linguist

AI models over-index on human language, so it's not just what you ask—but how you ask. Nuanced tweaks to word choice, conjugation, and sentence structure can all lead to different outputs. 

Example: In the image below, ChatGPT explains how subtle differences in word choice of AI prompts can lead to different outputs.

Related reading: How to write the best AI prompts

The technical aspects of prompt engineering

A well-crafted prompt is the foundation for successful interactions with AI models. As AI becomes more embedded in business operations, guiding them to provide accurate, useful outputs will be increasingly crucial for developers, business leaders, end-users—as well as in the emerging role of prompt engineers.

Here’s a quick overview of the technical side of prompt engineering:

Model calibration and fine-tuning

In addition to refining the prompt itself, you can also calibrate or fine-tune the AI model. This involves adjusting the models parameters to better suit specific tasks or datasets. This is an advanced practice, but can greatly improve model performance for specialized use cases.

Model architecture

The architecture of a language model—such as transformer-based designs like GPT or BERT—determines how it processes context, attention, and memory. For enterprise deployments, understanding the architectural trade-offs (e.g., model size, inference speed, latency) can influence which model is best suited for real-time AI customer service versus, say, data analysis.

Training data and tokenization

Models are trained on vast datasets, the content of which shape their knowledge and behavior. Similarly, the choice of tokenization—or the method the model uses to break language into pieces it can process—can also impact the model’s output. For instance, compound words or rare terms may be split inefficiently, which can dilute meaning and affect output relevance.

Model parameters

A range of configurable parameters influence the behavior of AI models. When crafting and testing AI prompts, adjusting these settings can help improve the consistency, relevance, and quality of the outputs.

This is usually done through an API, and requires some trial and error to find the optimal configuration, as different use cases may benefit from different parameter combinations.

Temperature and top-k sampling

These two LLM parameters control the randomness and diversity of AI-generated responses. They have a direct impact on the creativity and novelty of AI responses.

• Temperature determines how adventurous the model is. A low temperature (e.g., 0.2) yields more deterministic, safe responses that are ideal for, say, customer service AI agents. A higher temperature (e.g., 0.8+) is better for creative tasks in, say, marketing.
  

• Top-k sampling limits the model’s choices to the top “k” most likely next words, controlling variety and coherence. Depending on your goal, adjusting this up can reduce false responses (hallucinations) while adjusting it down encourages novelty.

Loss functions and gradients

Relevant in the model’s training phase, loss functions measure how far the model’s predictions are from the correct answer, and gradients guide how the model learns. While you don’t interact with these directly when prompting, understanding them is helpful in training or fine-tuning models—especially when trying to optimize for and stop specific behaviors.

Model differences

Not all LLMs behave or perform the same. Even when given identical prompts, different models—such as OpenAI’s GPT-4, Anthropic’s Claude, Google’s Gemini, or Meta’s LLaMA—can produce dramatically different outputs in tone, accuracy, reasoning depth, and factuality, and efficiency. It’s worth A/B testing prompts to determine which model is most aligned with your use case.

Dive deeper: Prompt engineering guide: LLM Settings

The future of prompt engineering

Prompt engineering is a rapidly evolving field. Transformer-based models are seeing widespread adoption across industries, research fields, and consumer applications. According to McKinsey, generative AI adoption leapt from 55% to 75% in 2024 among business leaders, signaling the growing importance of prompt engineering in competitive operations.

Some key prompt engineering trends to watch in 2025 include:

Evolving model capabilities

The latest AI models (OpenAI’s GPT-4.5 or Meta’s LLaMA 4) show a stronger grasp of natural language, and can often infer user intent with minimal input. Meanwhile, models such as Google’s Gemini 1 have demonstrated agent-like abilities to handle complex tasks autonomously. These capabilities could lessen the reliance on specialized prompt engineering.

Auto-prompting

AI systems can now generate their own prompts based on users' inferred goals or desired outputs. For example, Google’s Bard and OpenAI’s latest language models can automatically suggest prompts to refine the original, effectively guiding users without requiring them to know how to meticulously design prompts. 

Tool-using LLMs

Thanks to advances in agentic AI, LLMs will increasingly use external tools, APIs, and plugins to retrieve data and formulate outputs. As a result, prompt engineering will shift from focusing solely on language to also include tool orchestration. This will require prompt engineers to guide the AI in using the right tools at the right time for optimal results.

Multimodal prompt engineering

AI models have evolved to process a range of inputs—including text, images, audio, and code—so prompt engineering will expand to harness these multimodal capabilities. Crafting effective prompts will increasingly involve guiding AI to interpret, generate, and connect across multiple data formats, enabling more sophisticated and dynamic interactions.

Prompt templates

As AI models see greater adoption, organizations will likely develop libraries of optimized prompts, making prompt engineering a modular and reusable process. This will involve creating scalable templates and maintaining prompt libraries for various use cases. Prompt templates may soon be for sale.

Integration with domain-specific models

The rise of domain-specific AI models and small language models will enable prompt engineering to reliably deliver more precise outputs for specific use cases. Specialized models are trained on industry-specific data, leading to more accurate, relevant outputs in fields such as medicine, law, and finance.

The role of a prompt engineer

The role of prompt engineers is to ensure AI models live up to their full potential. Part model whisperer, part testing engineer—this dynamic, evolving role typically involves a combination of computer science, linguistics, and psychology.

Market context

As AI models become a business staple, organizations are seeking professionals who can extract reliable, domain-specific outputs from their models. 

According to one study, the global market for prompt engineering is expected to grow at 32.8% CAGR from 2024 to 2030. According to LinkedIn, prompt writing is among the top in-demand skills for AI-adjacent roles.

Technical skills

Effective, prompt engineers benefit from skills in natural language processing, basic programming (especially Python), and familiarity with AI APIs like OpenAI, Anthropic, or Google Vertex AI. Understanding model capabilities, limitations, and token constraints is also key to mastering prompt engineering and crafting optimized, scalable prompts.

A new discipline, "PromptOps," is also emerging. This focuses on managing, versioning, and deploying prompts in production workflows. This will involve tracking prompt performance through APIs, logs, and metrics.

Non-technical skills

Strong writing, critical thinking, and communication skills are essential. Prompt engineers must think creatively, reason through ambiguity, and structure language clearly to influence how AI interprets tasks and delivers value across different use cases.

Salary range

ZipRecruiter data indicates that prompt engineer jobs in the United States have an average annual pay of $62,977, with some salaries potentially reaching $95,500 or higher. 

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