Prompt engineering techniques

Prompt engineering techniques are strategies used to design and structure prompts, input queries or instructions, provided to AI models, particularly large language models (LLMs) such as openAI’s GPT-4, Google Gemini or IBM® Granite^TM. These techniques aim to guide generative AI (gen AI) systems to produce accurate, relevant and contextually appropriate responses, enabling users to achieve desired outputs effectively.

Large language models, which are built on advanced machine learning algorithms, are capable of understanding and generating human-like text. Prompt engineering leverages this capability by crafting inputs that help the model perform complex tasks, such as summarization, translation, creative writing, or problem-solving, with greater precision. By experimenting with different prompt structures, users can influence the behavior of LLMs to optimize their performance across diverse applications.

As generative AI continues to play a key role in various domains, understanding prompt engineering techniques has become essential for unlocking its full potential and tailoring AI models to meet specific needs efficiently.

A prompt is the input text or query provided to an AI model, such as a large language model to generate a response. It serves as the primary mechanism for guiding the model’s behavior, defining the task and setting the context for the interaction. The design of a prompt significantly impacts the quality and relevance of the output, making it essential to choose the right type of prompt for specific tasks.

To achieve the best results from AI models, it is essential to understand the various ways prompts can be structured to suit different tasks and objectives. There are three primary ways to structure the prompt: direct instructions, open-ended instructions and task-specific instructions.

Direct instructions are clear and specific commands that tell the AI exactly what to do. These prompts are ideal for straightforward tasks where the user has a clear expectation of the output. Direct prompts rely on the model’s ability to parse explicit instructions and generate responses that align closely with the command. The more detailed the instruction, the more likely the output will meet expectations.

Example:

In this case, the AI knows the exact format [a poem] and topic [nature] to generate the text.

Open-ended instructions are less restrictive and encourage the AI to explore broader ideas or provide creative and interpretive responses. These prompts are useful for brainstorming, storytelling or exploratory discussions where the user values variety and originality in the output. Open-ended prompts tap into the model’s generative capabilities without imposing constraints. The model relies on its training data to infer the best approach to the prompt, which can produce diverse or unexpected results.

Example:

Here, the AI has the freedom to decide what aspects of the universe to discuss, such as its origin, structure or scientific theories.

Task-specific instructions are designed for precise, goal-oriented tasks, such as translations, summarization or calculations. These prompts are often crafted with clarity and can include additional context or examples to help ensure accurate responses. Task-specific prompts leverage the model’s understanding of specialized tasks. They can incorporate advanced prompting techniques like few-shot prompting (providing examples) or zero-shot prompting (providing no examples but relying on the model’s pretrained knowledge).

Example: 

The model understands both the language translation task and the specific input text, enabling it to produce the desired output: “Bonjour.”

By understanding these types of prompts and the technical nuances behind them, users can craft prompts that guide AI models effectively, optimizing the quality and relevance of the responses. 

To maximize the effectiveness of AI models, prompt engineering employs a variety of techniques tailored to different tasks and objectives. The following are several key techniques, each explained with examples of prompts designed to achieve specific outcomes. 

To demonstrate the effectiveness of various prompt engineering techniques, Let’s check a single task as the central use case: explaining climate change. The task is framed as follows: 

Each technique approaches the task differently, offering varying levels of guidance, complexity, and methodology. Below, we explore how these techniques can be applied to this use case, with prompts tailored to highlight their unique capabilities.

Zero-shot prompting involves asking the model to perform a task without providing any prior examples or guidance. It relies entirely on the AI’s pretrained knowledge to interpret and respond to the prompt.^[1]

Example prompt:

The model is given no prior examples or additional context and must rely solely on its pretrained knowledge to generate the output.

Few-shot prompting includes a small number of examples within the prompt to demonstrate the task to the model. This approach helps the model better understand the context and expected output.^[2]

Example prompt:

By providing a few examples of how to explain other topics, the model is guided on the tone and level of simplicity expected for the climate change explanation.

CoT prompting encourages the model to reason through a problem step by step, breaking it into smaller components to arrive at a logical conclusion.^[3]

Example prompt:

The model is encouraged to think step by step, breaking down the explanation into smaller, logical parts for clarity.

Meta prompting involves asking the model to generate or refine its own prompts to better perform the task. This technique can improve output quality by leveraging the model’s ability to self-direct.^[4]

Example prompt:

The model generates its own prompt before attempting to explain the topic, potentially improving the relevance and quality of the output.

Self-consistency uses multiple independent generations from the model to identify the most coherent or accurate response. It’s particularly useful for tasks requiring reasoning or interpretation.^[5]

Example prompt:

The model produces multiple independent responses and selects the most consistent or coherent one as the final output.

This technique involves asking the model to generate background knowledge before addressing the main task, enhancing its ability to produce informed and accurate responses.^[6]

Example prompt:

The model generates background knowledge first (for example, greenhouse gases, global warming) to provide a more informed explanation.

Prompt chaining involves linking multiple prompts together, where the output of one prompt serves as the input for the next. This technique is ideal for multistep processes.

Example prompt:

Next prompt based on the previous response:

Next prompt based on the previous response: 

The task is divided into a chain of smaller prompts, with the output of each step feeding into the next for a more structured explanation.

Tree of thoughts prompting encourages the model to explore multiple branches of reasoning or ideas before arriving at a final output.^[7][8]

Example prompt:

The model explores multiple approaches to the explanation and selects the most effective one, providing a well-rounded output.

Retrieval augmented generation (RAG) combines external information retrieval with generative AI to produce responses based on up-to-date or domain-specific knowledge.^[9]

Example prompt:

The model combines its generative abilities with external knowledge to produce an informed explanation.

This technique integrates reasoning capabilities with external tools or application programming interfaces (APIs), allowing the model to use resources like calculators or search engines.^[10]

Example prompt:

The model integrates reasoning with external tools (for example, calculators or APIs) to analyze data and provide a data-driven explanation.

This method involves using the AI itself to generate and optimize prompts for specific tasks, automating the process of crafting effective instructions.

Example prompt:

The model automates the creation of an optimized prompt to improve the quality of its response.

Active-prompting dynamically adjusts the prompt based on intermediate outputs from the model, refining the input for better results.^[11]

Initial prompt

Follow-up prompt

The prompt dynamically evolves based on the intermediate output, refining the response over iterations.

Directional stimulus prompting (DSP) uses directional cues to nudge the model toward a specific type of response or perspective.^[12]

Example prompt:

The model is nudged toward a specific perspective or tone, influencing the framing of its explanation.

PALM integrates programming capabilities to augment the model’s reasoning and computational skills.^[13]

Example prompt:

The model combines programming with language generation to provide both a visualization and an explanation.

ReAct combines reasoning and acting prompts, encouraging the model to think critically and act based on its reasoning.^[14]

Example prompt:

This example illustrates how the model can combine analytical reasoning with actionable insights.

Reflexion allows the model to evaluate its previous outputs and refine them for improved accuracy or coherence.^[15]

Example prompt:

The model reflects on its previous output and iteratively improves it.

This technique integrates chain of thought reasoning across multiple modalities, such as text, images or audio.^[16]

Example prompt:

The model integrates reasoning across multiple modalities (text and images) to deliver a comprehensive explanation.

Graph prompting leverages graph-based structures to organize and reason through complex relationships between concepts or data points.

Example prompt:

The model uses graph-based reasoning to connect data points and generate an insightful explanation.

Thus, we can see how different prompt engineering techniques can be applied to a single task. By using the same task across methods such as zero-shot, few-shot, chain of thought and tree of thoughts, we can see how each technique structures the task differently and guides the AI to produce unique responses. These examples showcase the flexibility and creativity of prompt engineering in solving a variety of challenges. Readers are encouraged to try these prompt examples with different AI models or applications, such as IBM Granite models, OpenAI’s ChatGPT, Google’s Bard, Anthropic’s Claude, Cohere or AI21 Labs’ Jurassic. Doing so allows users to see how outputs vary and find what works best for their needs.

While prompt engineering techniques are powerful, they come with several challenges. Crafting effective prompts that consistently produce accurate outputs can be difficult, especially for tasks requiring complex reasoning, common sense understanding or nuanced responses. Hallucination is another common issue, where gen AI models generate information that is inaccurate or entirely fabricated. Relying on structured templates or fine-tuning the model can help mitigate some of these issues, but designing prompts that work across diverse scenarios remains a trial-and-error process. Additionally, balancing the general capabilities of artificial intelligence with task-specific objectives can be tricky, particularly for specialized or domain-specific tasks.

Prompt engineering techniques have a wide range of applications across various fields. In chatbots, they help refine generated responses to enhance user interactions in real-time. For developers, prompts can assist in generating code snippets or creating step-by-step tutorials for programming concepts. In education, they can simplify explanations or solve a math problem with detailed reasoning. Businesses use prompt engineering for decision-making by generating insightful AI outputs tailored to specific scenarios. On a large scale, these techniques are employed in content creation, customer support and automated workflows, making AI systems more efficient and adaptable to diverse tasks.

The future of prompt engineering techniques lies in advancing natural language processing to help ensure more accurate and relevant responses across diverse applications. As AI models evolve, their reasoning ability will improve, enabling them to handle more complex tasks with minimal prompting. We can also expect the development of smarter tools and frameworks to automate and optimize prompt creation, making interactions with AI more intuitive, efficient and personalized for users across various domains.

Prompt engineering techniques are essential for optimizing AI interactions and unlocking the full potential of large language models. By using structured approaches like zero-shot, few-shot, chain of thought and tree of thoughts, these techniques enable AI to tackle a wide range of tasks, from chatbots to decision-making and education. Despite challenges such as hallucinations and designing effective prompts, the applications of prompt engineering continue to expand across domains, providing smarter, more tailored AI outputs. As advancements in natural language processing and reasoning abilities progress, the future of prompt engineering promises even greater efficiency and adaptability. Readers are encouraged to experiment with these techniques across different AI models to explore their capabilities and refine their results.