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

Formula for Sequential Sub-Problem Solving

Example of a Complex Number Arithmetic Problem

A system is tasked with solving the following logic puzzle: 'A bakery started with 15 donuts. They sold one-third of them in the morning and then baked 6 more. How many donuts do they have now?' The system considers two distinct methods to find the answer.

Method A:

1. Generate a list of plausible final answers: {12, 14, 16, 18}.
2. For each plausible answer, check if it can be reached from the starting conditions. For '16', the check is: (15 - 15/3) + 6 = (15 - 5) + 6 = 10 + 6 = 16. This is consistent.
3. Select 16 as the final answer.

Method B:

1. Calculate the number of donuts sold: 15 / 3 = 5.
2. Calculate the number of donuts remaining after sales: 15 - 5 = 10.
3. Calculate the new total after baking more: 10 + 6 = 16.
4. State 16 as the final answer.

Which of these methods best illustrates the concept of forming a logical chain of intermediate steps that progresses from the initial problem to the final solution?

A system is tasked with solving the following problem: 'A company's profit was $50,000 in the first quarter. In the second quarter, it increased by 20%. In the third quarter, it decreased by $10,000 from the second quarter's profit. What was the profit in the third quarter?' Arrange the following actions into a logical, step-by-step reasoning path to arrive at the correct solution.

Constructing a Reasoning Path for a Word Problem

Formal Representation of a Solution as a Sequence of Reasoning Steps

Formula for Sequential Sub-Problem Solving

A problem-solving system is designed to break down a large initial problem ($p_0$) into a series of smaller steps. The process for generating the i-th step ($p_i$) is described by the formula: $p_i = G_i(p_0, \{p_{<i}, a_{<i}\}$) where $\{p_{<i}, a_{<i}\}$ represents all the preceding steps and their solutions. What does this formula imply about the system's approach to breaking down the problem?

Analyzing Problem Decomposition Strategies

A problem-solving process is defined by the formula $p_i = G_i(p_0, \{p_{<i}, a_{<i}\}$), where $p_i$ is the i-th sub-problem, $p_0$ is the original problem, and $\{p_{<i}, a_{<i}\}$ represents the set of all preceding sub-problems and their answers. This formula implies that all sub-problems can be determined and generated in a single step before any of them are solved.