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The rise of prompt ops: Tackling hidden AI costs from bad inputs and context bloat
The rise of "prompt ops": Tackling hidden AI costs from bad inputs and context bloat
Artificial Intelligence (AI) technology has been a major disruptor, ushering in the Fourth Industrial Revolution. While it has brought endless possibilities to the table, it also comes with certain hidden costs that many may not have taken into consideration. Among them are losses due to poorly constructed AI prompts and the 'context bloat.'
AI models learn from the data provided to them. The quality and type of input can directly impact the accuracy of an AI's results. When providing input to an AI model, the prompt must be tightly structured and devoid of any ambiguity. Ambiguity can make it tricky for an AI to deliver the correct output, which can lead to poor results and a waste of valuable resources.
Context bloat is another significant issue that hamper the efficiency of AI models. This occurs when an AI model is expected to develop and understand context over a massive amount of data. Context bloat can lead to an increase of computational resources - the model has to process up a large amount of data that may not directly influence its decisions. This further increases the overall cost and can limit the scalability of your AI model.
Enter "prompt ops." Prompt ops is a new approach to managing and optimizing the use of AI models, especially when it comes to crafting AI prompts and dealing with context bloat. It promotes a systematic and data-driven approach to crafting prompts for AI models. By perfecting this, you can significantly reduce the risk of bad inputs and manage computational optimization and scaling better. Prompt Ops focuses on the strategic and creative aspect of prompt engineering. It explores new ways to optimize the AI prompt, which can minimize some of the complexities related to context bloat and poorly constructed inputs.
In summary, PromptOps has
Source: ai Archives | VentureBeat, Link
#AI #ai #AI, ML and Deep Learning #chain of thought reasoning #FLOPS #GPUs #IDC #JSON #LLMs #o3 #prompt engineering #prompt ops #Retrieval-augmented generation (RAG) #transformer models #VB special issue #Vector Institute
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