Architectural Solutions for Context-Aware AI
An AI travel assistant is asked, 'What's the weather like at my destination and is my flight on time?' The underlying language model, operating solely on its pre-trained knowledge, provides a vague and unhelpful response. Propose two distinct system designs that could solve this problem: one that gathers all necessary external information before generating the full response, and another that calls for external information during the generation process. After describing your two designs, explain the single, fundamental objective that both of your proposed solutions achieve from a modeling perspective.
0
1
Tags
Ch.3 Prompting - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Evaluation in Bloom's Taxonomy
Cognitive Psychology
Psychology
Social Science
Empirical Science
Science
Related
Consider two language model systems. System A is provided with a set of relevant documents from a database at the same time it receives a user's query, before it begins generating a response. System B, in the middle of generating a response, pauses to execute a function that fetches real-time data, then uses that data to complete its answer. From a modeling perspective, what is the most accurate description of the fundamental goal shared by both systems?
Architectural Solutions for Context-Aware AI
The fact that Retrieval-Augmented Generation (RAG) gathers information before inference begins, while tool use accesses functions during inference, means that from a modeling perspective, they serve fundamentally different purposes in enhancing language model outputs.
The Agentic Perspective on Context Generation