You are tasked with reconstructing a prompt used in an AI-based customer support chatbot. The current prompt generates lengthy, detailed answers that are often overly verbose and unnecessary for the customer's inquiries. Your objective is to optimize this prompt to reduce model usage costs without compromising the quality of the responses. Which of the following strategies is the most effective in reducing the cost of using a Generative AI model while maintaining response relevance and clarity?
B. It improves the model’s generalization by exposing it to a wider variety of data points and scenarios. C. It creates a larger training dataset by duplicating and randomizing the existing data, which enhances model accuracy. D. It eliminates the need for any human intervention in the fine -tuning process. Which of the following decoding strategies would most likely result in generating creative and diverse text outputs while minimizing repetition, when using a generative AI model?
C. Use a rule-based model for summarization and a machine learning -based model for Q&A, combining them into a single service endpoint for deployment. D. Deploy both systems as a single pipeline, with the summarization model providing input to the Q&A model to improve the quality of answers. In the context of Generative AI models, which scenario best illustrates the use of greedy decoding for determining the output sequence?
based engine to route it to different models. B. IBM watsonx Model Management to dynamically select and orchestrate the models for different tasks based on real -time data analysis. C. IBM watsonx Orchestrator, which allows for the integration and management of multiple AI models and can dynamically route inputs to the appropriate model based on predefined criteria. D. IBM watsonx API Gateway to handle external data inputs, route them to different models, and ensure that each input is preprocessed in a low -latency manner. You are tasked with optimizing a Generative AI model by reducing the cost per inference without sacrificing the quality of output. Which of the following prompt modifications is the most effective approach to achieve this goal?
B. The model includes probabilistic estimates for treatment outcomes, which adds uncertainty to the recommendations and reduces their usability. C. The model produces highly creative treatment recommendations that are not based on standard medical guidelines. D. The model generates text that includes private patient information, violating data privacy regulations. E. The model generates biased recommendations based on incomplete or skewed training data, which disproportionately impacts certain patient demographics. You are optimizing a generative AI chatbot for concise responses to user queries, ensuring that it doesn’t over-generate unnecessary content. However, you observe that the model occasionally stops prematurely, cutting off relevant information. What configuration best addresses this issue without allowing for excessive output?
can you tell me about our product?” B. Supply explicit product details and constraints in the prompt: “List the latest features of the XYZ smartphone released in 2024, including its AI -powered camera and battery -saving mode.” C. Use subjective language in your prompts: “Can you describe why our product is the best on the market?” D. Ask open -ended questions without providing reference information: “What are the latest features of our product?” In developing an LLM-based conversational AI application using LangChain, you want the AI to perform complex tasks, such as answering questions based on dynamic knowledge from multiple sources (e.g., databases, APIs, etc.). Which approach using LangChain best supports this requirement by combining various tools into a structured workflow for the AI to follow?
