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Screaming at AI Doesn’t Work
- Mastering core concepts in AI, like context windows, is crucial for optimizing human-machine interactions and prompt engineering techniques.
- Context windows in AI models represent the range of contextual tokens a model can process during a conversation.
- Models have a fixed limit on the capacity to handle contextual information, leading to older conversation parts being discarded to make room for new inputs.
- There is a trend of increasing context window sizes in AI models, with state-of-the-art models handling up to a million tokens.
- Models sometimes struggle with processing and utilizing middle sections of extensive input data, showing biases towards information at the beginning and end.
- Crafting prompts with duplicated relevant information can help models better interpret inputs and improve response accuracy.
- Being clear, direct, and specific in prompts leads to stronger outputs, while role-based prompts align with models fine-tuned through Reinforcement Learning with Human Feedback.
- Description Before Completion technique helps in identifying how well a model understood a prompt, aiding in debugging and refinement.
- Understanding the training data sources and aligning prompts with the model's training format can enhance the quality of inferences.
- Ensuring Discourse Correctness in prompts maintains logical flow and coherence, contributing to reducing model hallucinations.
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