Example of Constructing a Contextual Prompt for a Subsequent Sub-Problem
To solve a multi-step problem, the context from previous steps is carried forward. For example, after establishing the answer to the first sub-problem ('Q: When did the environmental study start? A: The environmental study started in 2015.'), this entire question-answer pair is included in the input when posing the second sub-problem ('Q: When did the environmental study end?'). This ensures the model has the necessary information to proceed.
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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
Synthesizing Sub-Problem Answers for a Final Solution
Formula for Sequential Sub-Problem Solving
Prompting an LLM for the First Sub-Problem in a Sequence
Example of Constructing a Contextual Prompt for a Subsequent Sub-Problem
An AI assistant is tasked with planning a multi-day hiking trip. The task is broken down into a sequence of three steps. To solve the third and final step, 'Create a detailed meal plan,' two different approaches for providing the model with information are proposed.
- Approach X: The model is given the original task, the first question ('What is the weather forecast?') and its answer, the second question ('What gear is needed?') and its answer, and the final question ('Create a detailed meal plan.').
- Approach Y: The model is given only the second question ('What gear is needed?') and its answer, along with the final question ('Create a detailed meal plan.').
Which approach is more likely to produce a high-quality, contextually appropriate final answer, and why?
Constructing a Contextual Prompt for a Subsequent Sub-Problem
Synthesizing Sub-Problem Answers to Solve the Original Problem
An AI assistant is solving a complex problem by breaking it down into smaller, sequential questions. You need to construct the complete input that should be sent to the model to solve the third sub-problem. Arrange the following components in the correct logical order to create this input.
Diagnosing a Flawed AI Problem-Solving Process
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...
Create a Recursive, Context-Carrying Decomposition Plan for LLM-Assisted KPI Narrative Generation
Designing a Decomposition-Driven LLM Workflow for a High-Stakes Corporate Task
Evaluating and Redesigning a Decomposition Workflow Under Context and Cost Constraints
Debugging a Decomposition-Based LLM Workflow Using Recursive Sub-Problems and Contextual QA Pairs
Designing a Decomposition Workflow for Root-Cause Analysis of a Production Incident
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
Learn After
Example of Sequential Sub-Problem Solving: Environmental Study Duration
A large language model is tasked with solving a multi-step math problem. The overall problem is: 'A customer buys a laptop for $1200. The sales tax is 8%. What is the total cost?'. The model first solves a sub-problem: 'What is the sales tax amount for a $1200 laptop at an 8% tax rate?', and correctly answers '$96'. To solve the next step, which of the following inputs correctly provides the necessary context for the model to calculate the final total cost?
A language model is tasked with solving a multi-step problem. To solve the second sub-problem, it must be provided with the full context of the original problem and the previously solved sub-problem. Arrange the following components into the correct logical sequence to form a complete and effective input prompt for the model.
Constructing a Contextual Prompt for a Language Model