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Illustrating the Need for Decomposition in Generative Tasks
When assigning a complex generative task to a Large Language Model, such as writing a blog about AI risks, a simple, high-level prompt like 'write a blog about the risks of AI' can result in an output with an arbitrary and potentially disorganized structure. This example highlights the advantage of decomposing the problem first, for instance by creating an outline, to guide the LLM in producing a more structured and coherent result.
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Ch.3 Prompting - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Related
Divide-and-Conquer Paradigm
Example of a Classification Task for LLMs: Identifying AI Risks in a Document
Approaches to Multi-Step Reasoning in LLMs
Two-Step Problem Decomposition
Dynamic Problem Decomposition for Complex Reasoning
Compositionality in NLP
Outlining as a Method of Problem Decomposition for Generative Tasks
General Framework of Problem Decomposition
A team is using a large language model to automate complex tasks. They decide to implement a strategy where a main problem is broken down into a complete, fixed list of sub-problems before the model begins to solve any of them. The model will then solve each sub-problem in sequence. For which of the following tasks is this pre-defined decomposition approach LEAST likely to succeed?
Evaluating a Problem Decomposition Strategy for Multi-Hop QA
Illustrating the Need for Decomposition in Generative Tasks
Complex Reasoning Problems
Multi-hop Question Answering
A development team is building several applications powered by a large language model. Match each application's primary task with the most suitable strategy for breaking down the problem.
Designing a Decomposition-Driven LLM Workflow for a High-Stakes Corporate Task
Debugging a Decomposition-Based LLM Workflow Using Recursive Sub-Problems and Contextual QA Pairs
Evaluating and Redesigning a Decomposition Workflow Under Context and Cost Constraints
Designing a Decomposition-and-QA-Pair Workflow for Contract Review with Recursive Escalation
Stabilizing a Decomposition-Based LLM Workflow for a Regulated Customer-Email Triage System
Designing a Decomposition Workflow for Root-Cause Analysis of a Production Incident
Create a Recursive, Context-Carrying Decomposition Plan for LLM-Assisted KPI Narrative Generation
You are building an internal LLM assistant to answ...
You are designing an internal LLM workflow to answ...
You’re building an internal LLM workflow to answer...
Your team is rolling out an internal LLM assistant...
You’re building an internal LLM workflow to produc...
You’re building an internal LLM assistant to help ...
You’re leading an internal enablement team buildin...
Choosing and Justifying a Prompting Strategy Under Context and Quality Constraints
Designing a Prompting Workflow for a High-Stakes, Multi-Step Task
Diagnosing and Redesigning a Prompting Approach for a Decomposed Workflow
Stabilizing an LLM Workflow for Multi-Step Policy Compliance Decisions
Debugging a Multi-Step LLM Workflow for Contract Clause Risk Triage
Designing a Robust Prompting Workflow for Multi-Step Root-Cause Analysis with Limited Examples
Psychological Perspective on Problem Decomposition
Tool Use as Problem Decomposition in LLMs
Learn After
Improving Language Model Outputs for Complex Tasks
A user prompts a language model: 'Create a comprehensive business plan for a new online bookstore.' The model produces a generic, poorly organized document. Which of the following revised approaches would be most effective for generating a structured and detailed plan?
A user wants to use a language model to generate a well-structured blog post about the benefits of remote work. Arrange the following prompts in the most logical and effective sequence to guide the model from a high-level idea to a finished article.