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@@ -26,6 +26,21 @@ Nakshatra employs a combination of the following techniques to achieve its remar
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  - **Human-like Conversation Training**: Fine-tuned to imitate natural human conversational patterns.
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  - **Prototype Optimization**: This version is still in the prototype phase but showcases significant advancements in language coherence, tone, and emotional sensitivity.
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  ## Usage Code
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  ```python
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  import torch
 
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  - **Human-like Conversation Training**: Fine-tuned to imitate natural human conversational patterns.
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  - **Prototype Optimization**: This version is still in the prototype phase but showcases significant advancements in language coherence, tone, and emotional sensitivity.
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+ ## Limitations
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+ While Nakshatra represents a significant advancement in conversational AI, it is important to acknowledge its limitations:
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+ - **Prototype Status**: Nakshatra is currently in the prototype phase, which means it may not be fully optimized for all conversational scenarios. Users should be aware that further refinements and updates are expected.
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+ - **Factual Accuracy**: The model is designed to mimic human conversational styles and may generate responses that sound plausible but are factually incorrect. Users should verify critical information from reliable sources.
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+ - **Contextual Limitations**: Although Nakshatra exhibits deep contextual understanding, it may still struggle with complex or nuanced topics, leading to misunderstandings or irrelevant responses.
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+ - **Bias and Ethical Considerations**: Like all AI models, Nakshatra may inadvertently reflect biases present in the training data. Users should be mindful of this and approach interactions with a critical perspective.
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+ - **Dependence on Input Quality**: The quality of the model's responses is highly dependent on the clarity and context of the input it receives. Ambiguous or poorly structured queries may result in less coherent outputs.
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  ## Usage Code
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  ```python
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  import torch