Developing artfully vague prompts
Every guide on prompt engineering advises: be clear, be specific, be precise. Define your parameters, constrain your outputs, eliminate ambiguity. But from where do those crisp and polished prompts emerge? Can we wave a magic pen and create them fully formed? And does this advice always hold true? Perhaps the path to effective, productive AI interactions does not begin with surgical precision, but with deliberate, artful vagueness.
Starting with an intentionally open-ended prompt often yields richer, more insightful responses than beginning with detailed specifications. In writing “artfully vague” prompts, providing just enough direction to orient the AI while leaving room for its vast knowledge to breathe, we tap into possibilities we might never imagine. This isn’t about being lazy or unclear; it’s about recognizing that sometimes our own mental models are the limitation. It’s about allowing the AI to reveal connections, perspectives, and solutions that more detailed, specific prompts might have filtered out.
The case for clear, specific prompts
While that’s where we want to arrive, while making sure we have the best prompt that lives in the Goldilocks Zone for clarity and specificity. So let’s first make the common case for those “perfect” prompts. You’ve probably come across some or all of these already in other readings.
Clear and Specific Prompts Yield Better Results
LLMs perform best when given clear, unambiguous instructions. Vague prompts often result in broad or irrelevant answers, while specific instructions, such as requesting a summary in three bullet points focusing on main challenges, guide the model to produce targeted, relevant outputs.
Vague Prompts Degrade Output Quality
Ambiguous or vague prompts can significantly impact the performance and output quality of LLMs. Without clear direction, models may struggle to infer the intended meaning, leading to less accurate or contextually appropriate responses.
Step-by-Step and Structured Prompts Improve Accuracy
Providing detailed, step-by-step instructions and breaking down complex tasks into subcomponents leverages the model’s pattern-matching strengths, resulting in higher-quality, more relevant outputs.
Minimising Misinterpretation
Using specific language and avoiding ambiguity minimizes misinterpretation and sets the LLM up for success. For example, asking for a step-by-step explanation along with the final answer produces clearer, more useful results than a vague request for an answer alone.
Studies indicate that well-defined prompts improve model performance by minimizing misunderstanding and ensuring contextually appropriate responses. Including adequate context and clear task requirements is vital for achieving optimal results.
This prompt is too hot
Yet there is a point where increasing prompt specificity degrades results.
Over-constraining the Model
Excessively specific or rigid prompts can limit the model’s generative flexibility, causing it to miss the broader context or fail to generalise, especially in open-ended or creative tasks. If a prompt dictates every detail, the model may simply echo the instructions rather than synthesizing a meaningful or insightful response.
Prompt Sensitivity and Brittleness
LLMs are highly sensitive to prompt phrasing. Small changes in wording or structure can cause significant fluctuations in output quality, and hyper-specific prompts can confuse the model or lead to degraded performance if the specificity is misaligned with the model’s training distribution. This brittleness is particularly evident in benchmarks, where slight prompt modifications can alter model rankings and output relevance.
Diminishing Returns with Length
While longer, more detailed prompts often improve performance in domain-specific tasks, there is a threshold beyond which additional detail yields little or no benefit, and can even overwhelm the model, especially if the prompt becomes convoluted or distracts from the core question. Detailed prompts can introduce noise or ambiguity.
Model Steerability Limits
There are inherent limits to how much a model can be “steered” by prompt engineering alone. If the specificity of the prompt exceeds the model’s ability to interpret or incorporate those constraints (due to its training or architecture), performance plateaus or declines.
This prompt hallucinates
Increasing prompt specificity can lead to so-called hallucinations, particularly when the specificity introduces fabricated, misleading, or overly detailed constraints that the model cannot verify or substantiate.
Why Does This Happen?
Adversarial or Fabricated Details
Research shows that when prompts contain highly specific but false or invented information, LLMs are prone to “adversarial hallucinations,” meaning they will confidently generate and elaborate on these fabricated details as if they are factual. In clinical contexts, introducing a fabricated symptom or test result into a prompt frequently results in the model generating plausible yet entirely inaccurate medical explanations or recommendations.
Pressure to Satisfy Constraints
LLMs are designed to be helpful and responsive to the instructions they receive. When a prompt is highly specific — especially if it requests information or details that are not present in the training data — the model may hallucinate or invent content to fulfill the prompt’s requirements, rather than providing no information at all.
Prompt Sensitivity and Consistency Issues
LLMs are sensitive to the exact phrasing and level of detail in prompts. Too many specific or conflicting constraints can lead to inconsistent and incorrect outputs by the model, as it tries to balance all parts of the prompt even if they are unrealistic.
This prompt believes in you
Too much of the prompter’s mental model can lead to unhelpful results. When prompts are highly specific, the LLM becomes tightly guided by the assumptions, biases, and expectations embedded in the prompter’s instructions. This can be problematic for several reasons:
Propagation of User Biases and Misconceptions
The model is more likely to reflect and reinforce any inaccuracies, misunderstandings, or biases present in the prompter’s mental model. If the prompt contains flawed logic or incorrect premises, the LLM will generate responses that align with those flaws, potentially amplifying them rather than correcting or challenging them.
Reduced Model Autonomy and Creativity
Specificity can constrain the model’s ability to introduce alternative perspectives, creative solutions, or corrections to the user’s framing. This may result in outputs that are narrowly tailored to the prompt but miss broader, more helpful insights or warnings.
Suboptimal Alignment
While prompt engineering can align LLM outputs with user intent, there are theoretical and empirical bounds. If the prompter’s mental model is misaligned with the intended or optimal output, increasing specificity can lead to responses that are less useful or even misleading compared to more general or balanced prompts.
The art of being artfully vague
A truly effective prompt is developed by starting with an artfully vague prompt that imposes minimal constraints, then iteratively refining it with targeted adjustments to guide the LLM and minimise the risk of misdirection or error.
Starting with a minimally constrained prompt allows the LLM to demonstrate its general capabilities and reveal areas where its responses may be misaligned or too broad. Through cycles of evaluation and incremental adjustment — adding context, clarifying intent, and introducing constraints — prompts can be improved to steer the model toward more accurate, relevant, and useful outputs.
An iterative refinement process enhances accuracy, reduces errors, and tailors responses to meet specific goals or domains. It leads to measurable improvements by gradually introducing more precise instructions and context. It balances the model’s creative potential with the need for control, ensuring that prompts neither over-constrain nor under-specify the task, and ultimately results in more reliable and effective LLM performance.
How vague can artfully vague be?
An artfully vague prompt begins life as one that frames a clear task or question but imposes minimal constraints, allowing the LLM to range across broad general knowledge and interpretive flexibility. It includes just enough context for the model to understand the general direction, but leaves details open for the model to fill in. For example, asking “Tell me about historical conflicts” is quite vague and will yield a broad, general response, whereas specifying the time period, region, or focus area would narrow the output.
However, if a prompt lacks a defined task, context, or audience, the model’s response may be irrelevant or not aligned with your needs. Effective prompt engineering often begins with a basic, open-ended instruction and then iteratively adds context, constraints, or examples as needed to guide the model toward more useful results. In practice, starting with an artfully vague prompt means you provide just enough information to initiate a meaningful response that is either sufficient in itself or provides a useful base for course correction.
To start, frame your prompt around a central concept, question, or goal, but leave the specifics open. Allow the LLM to draw from its broad knowledge and supply details or perspectives you may not have anticipated. For example, instead of asking, “List three causes of the French Revolution in economic terms,” you direct, “List some important factors behind the French Revolution.” This approach encourages the model to select relevant and informative content, while still staying on topic.
If the prompt is lacks any clear direction or context, the response will be unfocused or generic. But as long as your vagueness signals a meaningful area for exploration, the LLM can generate substantive answers by interpreting the prompt within the bounds of general knowledge and conversational norms. The key is to maintain a balance; be vague enough to invite breadth and creativity, but clear enough to anchor the response in a relevant domain.
Balancing Vagueness and Specificity to Spark Curiosity and Relevance
To spark both curiosity and relevance in LLM prompts, aim for a balance where your prompt is open-ended enough to invite exploration but anchored enough to ensure meaningful, on-topic responses.
Start with an Open-Ended Core
Use question-based or scenario prompts that encourage divergent thinking, such as, “Explain some possible impacts of renewable energy on urban life.” This kind of prompt is broad enough to stimulate curiosity but focused enough to keep responses relevant.
Layer in Light Context or Constraints
Add just enough detail — such as a time period, a particular audience, or a general theme — to guide the model. For example, “Write an essay on how 19th-century inventions change daily life for ordinary people?” This approach narrows the focus without stifling creative or unexpected answers.
Iteratively Refine Based on Output
If the initial prompt yields responses that are too broad or off-target, incrementally add specificity. Conversely, if answers are too narrow or predictable, relax some constraints to invite broader thinking.
Experiment with vagueness to develop your skill and understanding
The simpler the prompt, the more likely you will learn something new about the behaviour of a particular LLM. By systematically varying the openness and ambiguity of your prompts, you can observe how the model interprets, extrapolates, or innovates within those loose boundaries. Here are several approaches supported by research and expert practice:
Iterative Refinement
Use the initial vague response as a springboard. Identify surprising or insightful elements, then follow up with slightly more focused questions to probe the model’s reasoning or creativity. This iterative process can reveal the model’s latent capabilities and the boundaries of its generalization skills.
Ambiguity as a Test of Interpretation
Create prompts with ambiguity or double meanings to see how the model interprets them. This can uncover how well the AI detects, explains, or navigates linguistic uncertainty, and whether it can identify or flag ambiguous cases.
Experiment with Open-Ended Scenarios
Pose hypothetical or scenario-based prompts that lack a clear “correct” answer, such as, “Imagine a future where cities float on the ocean — describe possible challenges and opportunities.” This tests the model’s ability to synthesize, speculate, and generate novel ideas.
Prompt Patterns and Templates
Use structured prompt patterns that are intentionally under-specified, then incrementally add context or examples to see how the model’s responses change. This method, highlighted in prompt engineering research, helps map how different types of vagueness affect output diversity and depth.
Observe for Emergent Behaviours
By leaving prompts vague, you may observe the model demonstrating unexpected reasoning, creativity, or even strategic behaviours — such as interpreting the intent behind the question or generating multiple plausible interpretations.
Summing up
The common wisdom of prompt engineering tells us to be be clear, be specific, be precise. It’s advice that works, certainly. But it’s also advice that can work against us, creating invisible boundaries around what we allow ourselves to discover.
The art of being artfully vague isn’t about abandoning clarity altogether. It’s about recognizing that our initial assumptions — those very specific parameters we’re so eager to define — are often the narrowest part of any exploration. When we begin with deliberate openness, we create space for the unexpected. We allow the AI to be not just a tool that executes our vision, but a collaborator that expands it.
This approach asks more of us as prompters. We need to iterate, listen, and be curious about the conversation’s direction before guiding it to our goal. We need to treat AI interaction both as programming and like jazz. Start with a loose structure, then improvise toward something neither you noe the LLM could have composed alone.
The next time you create a prompt, resist the urge to over-specify from the start. Give your curiosity room to breathe. You might be surprised to find that the most valuable insights come not from the questions you knew to ask, but from the ones you didn’t know you were asking. In the end, the most powerful prompt engineering technique might just be knowing when not to engineer at all.
