Common Prompting Challenges

While crafting prompts is the way we communicate our intentions to Large Language Models, it's not always a smooth process. You might write what seems like a perfectly clear instruction, only to receive an unexpected, incorrect, or unhelpful response. Understanding the common hurdles you might encounter can make you a more effective prompter. Let's look at some frequent challenges.

Ambiguity and Lack of Specificity

LLMs work best with clear, unambiguous instructions. If your prompt is too vague or open to interpretation, the model might guess your intention, and its guess could be wrong. It lacks the real-world context and common sense reasoning that humans use to fill in the gaps.

Consider this prompt:

Tell me about dogs.

This is very broad. The LLM could respond with:

• The scientific classification of dogs.
• Information about popular dog breeds.
• Tips on dog care.
• Stories about famous dogs.
• A comparison between dogs and cats.

If you specifically wanted information on common breeds, a more effective prompt would be:

List 5 popular dog breeds in the United States and provide a brief description (1-2 sentences) of each.

This revised prompt clearly specifies the desired content (popular breeds), the quantity (5), the region (United States), and the format (brief descriptions of 1-2 sentences). Remember the principle from the "Providing Clear Instructions" section: the more specific you are, the higher the chance the LLM will generate the output you actually want.

Model "Hallucinations" or Fabricated Information

One peculiar behavior you might observe is an LLM generating text that sounds plausible and confident but is factually incorrect or entirely made up. This is often referred to as "hallucination." It happens because LLMs are fundamentally prediction machines. They generate text based on patterns learned from their vast training data, aiming for statistical likelihood rather than factual accuracy. If generating a fabricated "fact" fits the pattern better than stating uncertainty or retrieving correct information, the model might do so.

For example, you might ask:

Who invented the fuzzy stapler?

An LLM might confidently reply:

The fuzzy stapler was invented by Dr. Eleanor Piffle in 1983.

If Dr. Piffle and the fuzzy stapler don't exist, this is a hallucination. The model constructed a response that looks like a factual answer based on similar sentence structures it has seen.

What to do about it:

• Be skeptical: Treat LLM outputs, especially factual claims, with caution.
• Cross-reference: Verify important information using reliable sources.
• Prompt for sources: Ask the LLM to provide sources for its claims (though be aware it might sometimes hallucinate sources too).
• Specify grounding: Instruct the model to base its answer only on provided text if you supply context.

Overly Generic or Repetitive Responses

Sometimes, an LLM's output can feel bland, generic, or repetitive, especially for creative tasks or when the prompt is simple. The model might fall back on the most common patterns in its training data, leading to uninspired text.

Prompt:

Write a poem about a cat.

Response (potentially):

A fluffy cat, so sleek and neat, With silent paws and gentle feet. It naps all day in sunny spots, And dreams of mice it hasn't caught.

While technically correct, it's quite generic. To get a more interesting response, you could try:

• Adding constraints: Write a short, humorous poem about a clumsy cat trying to catch a laser pointer, written in the style of Dr. Seuss.
• Setting a persona: You are a grumpy old pirate. Write a sea shanty about your annoying but beloved ship's cat.
• Providing more context: Write a poem from the perspective of a cat who secretly believes it's the ruler of the house, observing its human subjects.

Difficulty Following Complex Instructions

LLMs can handle multi-part instructions, but their ability has limits. If a prompt contains too many steps, constraints, or conditional logic, the model might miss parts, get confused, or execute them incorrectly.

Consider a complex prompt:

Summarize the provided article in exactly 5 bullet points. Then, identify the main sentiment (positive, negative, or neutral). Finally, extract all company names mentioned and list them alphabetically, ignoring any companies mentioned only in the references section.

An LLM might provide a good summary but fail to extract the companies correctly or misjudge the sentiment.

Strategies:

• Break it down: Instead of one complex prompt, consider multiple simpler prompts in sequence. First, ask for the summary. Then, in a separate prompt, ask for the sentiment based on the original article or the summary. Finally, ask for the company list.
• Use formatting: Clearly structure your instructions using lists or numbered steps within the prompt itself.
• Iterate: If the first attempt fails, simplify the instructions or tackle one part of the task at a time.

Sensitivity to Prompt Phrasing

You might find that slightly different ways of asking for the same thing produce noticeably different results. The exact words, punctuation, and sentence structure can influence the model's interpretation and subsequent output.

Example:

• Prompt A: Explain the concept of photosynthesis simply.
• Prompt B: Describe how photosynthesis works for a five-year-old.

Prompt B will likely generate a much simpler explanation using analogies, whereas Prompt A might produce a technically accurate but more complex description. This sensitivity means that experimenting with different phrasings is often necessary to achieve the desired outcome. If you aren't getting what you want, try rephrasing your request.

Bias in Responses

LLMs learn from vast amounts of text data generated by humans. Unfortunately, this data contains various societal biases (related to gender, race, age, etc.). Consequently, LLMs can sometimes reflect or even amplify these biases in their responses, even if the prompt didn't intend to elicit them.

For example, a prompt asking for typical characteristics of a certain profession might generate stereotypical descriptions if those stereotypes are prevalent in the training data.

What to know:

• Be aware: Understand that biases can exist in LLM outputs.
• Critically evaluate: Assess responses for fairness and potential bias.
• Prompt carefully: Avoid phrasing that might unintentionally encourage stereotypes. You can sometimes explicitly ask the model for a neutral or balanced perspective, though success isn't guaranteed. Addressing bias in AI is an ongoing area of research and development.

Encountering these challenges is a normal part of learning to work with LLMs. Don't be discouraged! View each unexpected output as a learning opportunity. By recognizing these common issues, experimenting with your prompts, and iterating on your instructions, you'll become much better at guiding LLMs to generate useful and accurate responses.