Top 10 LLM Prompt Mistakes That Are Silently Killing Your AI's Performance

Tiffany Updated on Sep 5, 2025 Filed to: Blog

Every user has faced this: you craft a seemingly perfect LLM prompt, only to get a messy, unusable, or completely misguided response. This isn't a model failure, but a result of small, "silent" mistakes, a common pain point in LLM prompt engineering.

Based on community discussions from platforms like Reddit and Stack Overflow, this guide identifies the 10 most common LLM prompt mistakes. For each, we provide 'before' and 'after' examples to help you immediately fix your prompts and dramatically improve your AI's output.

10 Common Mistakes of LLM Prompt & Their Fixes

Mistake 1: Being Too Vague (The "Figure It Out Yourself" Mistake)

The Problem: Using subjective or imprecise language like "make it more professional" or "summarize this." The LLM has no context for what "professional" means to you.

Result: This leads to non-deterministic, inconsistent outputs that are impossible to rely on in a production environment. A vague LLM prompt creates unpredictable behavior.

Bad Example: Summarize the attached article.
Good Example: Summarize the attached article into a 3-bullet point list. Each bullet point should be a complete sentence and capture a key insight for a busy tech executive.

💡Recap: Always replace ambiguity with specificity. Define your terms and constraints.

Mistake 2: Forgetting to Assign a Role (The "Who Am I?" Mistake)

The Problem: Without a persona, the LLM defaults to a generic, helpful assistant. Assigning a role (e.g., "You are a senior copywriter") can greatly focus its output.

Result: Without a defined role, the model lacks a persona or tone, making it difficult to generate targeted, persuasive, or technically accurate content. This is a fundamental aspect of a good LLM prompt.

Bad Example: Write a product description for a new coffee maker.
Good Example: You are an expert e-commerce copywriter specializing in luxury home goods. Write a 150-word product description for a new stainless steel coffee maker. Focus on the benefits of its high-speed brewing and consistent temperature control. Use a persuasive and aspirational tone.

💡Recap: An LLM prompt that starts with a role is almost always better than one that doesn't.

Mistake 3: Not Defining the Output Format (The "Guess the Structure" Mistake)

The Problem: If you don't explicitly request a specific format like JSON, Markdown, or a simple list, the model will return an unstructured block of text that is impossible to parse reliably.

Result: This is a classic developer pain point. Without a structured output, your LLM is not an API; it's just a text generator. This prevents you from building automated workflows and applications.

Bad Example: Extract the user's name, email, and company from this text: [text block].
Good Example: Extract the user's name, email, and company from the following text. Return the output as a JSON object with the keys "name", "email", and "company". If a value is not found, use null.
Text: [text block]

💡Recap: Treat the LLM like an API. Clearly define the data schema you expect in return.

Mistake 4: Providing No Examples (The "Zero-Shot Gamble")

The Problem: Expecting the model to understand a complex or nuanced task without a clear example of what you want. This is especially true for custom formatting or reasoning tasks.

Result: Without a guiding example, the LLM will fall back on its general training data, which may not align with your specific needs. This is a common pitfall in LLM prompt design.

Bad Example: Classify customer feedback into "Positive", "Negative", or "Neutral".
Good Example: Classify customer feedback into "Positive", "Negative", or "Neutral". Follow the examples below.

Feedback: "The app is a bit slow but the features are great."
Classification: "Neutral"

Feedback: "I can't believe how easy this was to set up!"
Classification: "Positive"

Feedback: [Your new feedback to classify]

💡Recap: One or two good examples (Few-Shot Prompting) are worth more than a hundred words of instruction.

Mistake 5: Overloading a Single Prompt (The "Do Everything at Once" Mistake)

The Problem: Trying to make one LLM prompt perform multiple distinct tasks (e.g., summarize, translate, and then extract keywords). This leads to degraded performance on all tasks.

Result: An overloaded prompt confuses the model, as it tries to balance competing goals. This leads to a degradation of quality and often increases latency.

Bad Example: Summarize this article, translate the summary to Spanish, and then give me a list of 5 relevant SEO keywords.
Good Example: This is a multi-step process. You must send these prompts one at a time, using the output of the previous step as the input for the next.
Step 1: Summarize this article in English.
Step 2: Translate the following text to Spanish: [output from step 1]
Step 3: Extract 5 relevant SEO keywords from the following text: [output from step 1]

💡Recap: For complex workflows, chain simple, single-purpose prompts together for more reliable and higher-quality results.

Mistake 6: Ignoring the Context Window (The "Amnesia" Mistake)

The Problem: Assuming the model remembers information from previous, unrelated conversations or expecting it to know details you haven't provided in the current context.

Result: An LLM prompt only has access to the information provided in the current request. Without the necessary context, the model will hallucinate or provide a generic response.

Bad Example: Based on the document I sent you yesterday, what are the key Recaps? (This won't work).
Good Example: I am providing you with a document below. Please act as a business analyst and identify the top 3 strategic risks mentioned within it. [Upload the document or paste the full text of the document here]

Advanced Tip: For very large documents or external knowledge bases, manually pasting content into the prompt is not scalable. Look into advanced techniques like Retrieval-Augmented Generation (RAG) to provide context automatically.

💡Recap: Every LLM prompt should be self-contained. Always provide all the necessary context for the task at hand.

Mistake 7: Not Using Delimiters (The "Confusing the Input" Mistake)

The Problem: When you mix instructions with user input or external text, the model can get confused about which part is the instruction and which part is the data to be processed.

Result: This can lead to security vulnerabilities (prompt injection) and inconsistent outputs, as the model may misinterpret instructions as part of the content it needs to process. A properly delimited LLM prompt is a secure LLM prompt.

Bad Example: Please summarize the following customer review for me. The customer was unhappy with the shipping time but liked the product.
Good Example:
Please summarize the customer review below, which is delimited by triple backticks.
```
Paste the entire user review here.
```

💡Recap:

Use clear separators like ###, ``` , or XML tags to cleanly separate instructions from the content you want the LLM to work on.

Use triple backticks (```) as your delimiter. While other symbols work, this is the most reliable option to prevent conflicts with punctuation.

You can place multiple items inside a single delimited block for batch processing, which is far more efficient than sending each item individually.

The model can distinguish between items on its own, but it's a good practice to add your own numbering inside the delimited text if you need a specific, structured output like a numbered list.

Mistake 8: Not Specifying Negative Constraints (The "Don't Do This" Mistake)

The Problem: You've told the LLM what to include, but not what to exclude. This can lead to the model including boilerplate text, disclaimers, or specific topics you don't want.

Result: The model will generate unnecessary "fluff" or extraneous text, forcing you to manually edit the output. This extra work compromises efficiency and defeats the purpose of using an automated system.

Bad Example: Write a summary of the new AI model.
Good Example: Summarize the key findings of the article. Do not include any mention of company names, stock prices, or market predictions.

💡Recap: Explicitly use words like "do not," "avoid," or "exclude" to set clear boundaries for your prompt.

Mistake 9: Ignoring Length or Verbosity (The "TL;DR" Mistake)

The Problem: You haven't specified the desired output length or level of detail. The model might give you a sentence when you need a paragraph, or a full essay when you just need a brief summary.

Result: Without a clear length requirement, outputs become inconsistent and require manual editing.

Bad Example: Explain how a neural network works.
Good Example: Explain how a neural network works in under 100 words, using a cooking recipe analogy. Be sure to cover the concepts of layers and weights.

💡Recap: Always specify length (e.g., "under 100 words," "a single paragraph"), format (e.g., "a list," "a table"), or level of detail (e.g., "briefly," "in-depth").

Mistake 10: Using a Conversational Tone for Technical Tasks (The "Friendly but Vague" Mistake)

The Problem: You're using a conversational or overly friendly tone for a technical task that requires a precise, structured output. The model may prioritize being "helpful" over being "accurate."

Result: While a chatty tone is fine for conversation, it introduces ambiguity into technical prompts, compromising accuracy.

Bad Example: Hey, can you help me extract some data from this text? I need the names and stuff.
Good Example: TASK: Extract all full names and their corresponding titles from the provided text. Return the output as a JSON object with keys "name" and "title". If a value is not found, use null.

💡Recap: For technical or programmatic tasks, treat your prompt like a command. Use direct, declarative language and avoid unnecessary filler words.

Conclusion

By moving from vague instructions to specific commands, assigning roles, defining formats, and providing context, you are shifting from a casual user to a true LLM prompt engineer.

The core principle behind all these fixes is clarity and explicitness. Treat the LLM as a brilliant but extremely literal intern. The more precise your LLM prompt, the more powerful your results will be. LLM prompt engineering is less about "AI whispers" and more about clear, deliberate instruction.

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Tiffany

Tiffany has been working in the AI field for over 5 years. With a background in computer science and a passion for exploring the potential of AI, she has dedicated her career to writing insightful articles about the latest advancements in AI technology.

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