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31 lines
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31 lines
2.2 KiB
# Pitfalls of LLMs |
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## LLM Pitfalls |
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In this section, we'll discuss some of the common pitfalls that you might encounter when working with Language Models (LLMs), particularly in the context of prompt engineering. By understanding these pitfalls, you can more effectively develop prompts and avoid potential issues that may affect the performance and utility of your model. |
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### 1. Model Guessing Your Intentions |
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Sometimes, LLMs might not fully comprehend the intent of your prompt and may generate generic or safe responses. To mitigate this, make your prompts more explicit or ask the model to think step-by-step before providing a final answer. |
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### 2. Sensitivity to Prompt Phrasing |
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LLMs can be sensitive to the phrasing of your prompts, which might result in completely different or inconsistent responses. Ensure that your prompts are well-phrased and clear to minimize confusion. |
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### 3. Model Generating Plausible but Incorrect Answers |
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In some cases, LLMs might generate answers that sound plausible but are actually incorrect. One way to deal with this is by adding a step for the model to verify the accuracy of its response or by prompting the model to provide evidence or a source for the given information. |
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### 4. Verbose or Overly Technical Responses |
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LLMs, especially larger ones, may generate responses that are unnecessarily verbose or overly technical. To avoid this, explicitly guide the model by making your prompt more specific, asking for a simpler response, or requesting a particular format. |
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### 5. LLMs Not Asking for Clarification |
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When faced with an ambiguous prompt, LLMs might try to answer it without asking for clarification. To encourage the model to seek clarification, you can prepend your prompt with "If the question is unclear, please ask for clarification." |
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### 6. Model Failure to Perform Multi-part Tasks |
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Sometimes, LLMs might not complete all parts of a multi-part task or might only focus on one aspect of it. To avoid this, consider breaking the task into smaller, more manageable sub-tasks or ensure that each part of the task is clearly identified in the prompt. |
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By being mindful of these pitfalls and implementing the suggested solutions, you can create more effective prompts and optimize the performance of your LLM. |