What approach is used to efficiently manage multiple tasks for an LLM?
Select the best answer below, then click Submit.
A. Singularizing
B. Sequential prompting
C. Prompt bundling
D. Prompt chaining
Answer (2)
The best approach to efficiently manage multiple tasks for an LLM is Prompt Chaining. This technique links prompts together, allowing the model to build on previous outputs for coherent responses. It is effective for tackling complex queries by maintaining context throughout the process.
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The best approach to efficiently manage multiple tasks for a Language Model (LLM) is (C) Prompt bundling.
Prompt bundling refers to the method of combining multiple prompts or questions into a single input query for a language model. This approach is efficient in managing multiple tasks as it allows for the simultaneous processing of several queries, which reduces computational overhead and improves response times.
Here's a step-by-step explanation of how prompt bundling works:
Identify Multiple Tasks: First, tasks that need to be addressed by the language model are identified. These could be questions, calculations, or any forms of processing that the model can handle.
Combine Queries: The identified tasks are then combined into a single, coherent input. This is done by structuring the prompt in such a way that the language model understands it needs to address multiple items.
Process Prompt: The bundled prompt is fed into the language model, which processes the entire input in one go. This is more efficient than handling each task separately, as the model can leverage its context mechanism to understand and respond to the bundled queries collectively.
Output Generation: The language model generates responses to all the individual tasks included in the bundled prompt, often in a single output.
This technique is particularly useful in situations where computational resources are limited or where quick response times are needed, such as in real-time applications. It enhances the ability of LLMs to manage parallel tasks efficiently, making them more powerful and versatile.
