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- What are the key components of Infermatic.ai's architecture that contribute to its performance in question answering tasks?
- How does the use of neural networks in Infermatic.ai's architecture impact its ability to generate coherent and relevant text?
- What role does the integration of natural language processing (NLP) techniques play in enhancing Infermatic.ai's performance in text generation tasks?
- Can you explain the concept of prompt engineering in the context of Infermatic.ai and its application in improving question answering and text generation?
- How does the size and complexity of Infermatic.ai's training data influence its ability to generalize and adapt to new, unseen tasks?
- What are the trade-offs between the use of pre-trained language models versus fine-tuning on specialized datasets in Infermatic.ai's architecture?
- How does the scalability of Infermatic.ai's architecture affect its performance in handling large volumes of input data?
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