Designing a Prompting Workflow for a High-Stakes, Multi-Step Task
You are deploying an LLM inside a corporate workflow to draft a first-pass analysis for a high-stakes internal decision memo (e.g., whether to approve a vendor contract). The input is a long, messy bundle of notes and constraints, and early pilots show two failure modes: (1) when asked directly, the model misses key constraints and produces confident but shallow conclusions; (2) when you add a generic “let’s think step by step” instruction, it produces lengthy reasoning but sometimes never states a clear final recommendation.
Write a proposed prompting workflow (you may describe it as a sequence of prompts) that uses: in-context learning with demonstrations, chain-of-thought prompting (including a zero-shot trigger where appropriate), least-to-most prompting, explicit problem decomposition, and an iterative self-refinement loop. Your answer must explain how these techniques work together (not independently) to reduce the two failure modes above, and must justify at least two tradeoffs you are making (e.g., latency/cost vs. accuracy, transparency vs. risk of overlong reasoning, fixed vs. dynamic decomposition). Conclude by giving one concrete example of a “final answer formatting” instruction you would include to ensure the model always outputs a clear recommendation at the end.
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Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Ch.3 Prompting - Foundations of Large Language Models
Ch.5 Inference - Foundations of Large Language Models
Ch.1 Pre-training - Foundations of Large Language Models
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Chain-of-Thought as a Search Process
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Limitations of Simple Chain-of-Thought Prompting
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Alternative Trigger Phrases for Zero-Shot CoT Prompting
Incomplete Answers as a Potential Issue in Zero-Shot CoT Prompting
A developer is trying to improve a language model's ability to solve multi-step word problems. They compare two prompting strategies.
Strategy 1: Provide the model with a new word problem and ask for the final answer directly.
Strategy 2: Provide the model with a new word problem, but first show it an example of a similar problem where the solution is explicitly broken down into logical, sequential steps before reaching the final conclusion.
Why is Strategy 2 generally more effective for improving the model's reasoning on complex tasks?
Improving a Prompt for a Multi-Step Problem
Few-Shot Chain-of-Thought (CoT) Prompting
Practical Limitations of Chain-of-Thought Prompting
The primary benefit of a prompting technique that demonstrates a step-by-step reasoning process is that it permanently modifies the language model's internal weights, making it inherently better at solving similar problems in the future, even without the detailed prompt.
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Improving Least-to-Most Prompting with Advanced Techniques
Improving Problem Decomposition in Least-to-Most Prompting
An AI developer needs a large language model to solve a complex, multi-step logic puzzle that requires deducing a final answer from a series of interdependent clues. Initial attempts to solve the puzzle by providing the full puzzle and a few examples of other solved puzzles have consistently failed. Which of the following prompting strategies is the most effective next step, and why?
Analyzing a Problem-Solving Approach
A language model is tasked with solving the following logic puzzle: 'Sarah, David, and Emily are a doctor, a lawyer, and an engineer. The doctor is Emily's sister. David is not the lawyer.' To solve this complex problem, it is broken down into a series of simpler, sequential sub-problems. Arrange the following sub-problems in the correct logical order that builds towards the final solution.
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Three-Step Framework for Self-Refinement in LLMs
Ideal Self-Refinement without Additional Training
Fine-Tuning LLMs for Self-Refinement Tasks
Task-Specific Models as an Alternative for Refinement
Self-Refinement as an LLM Alignment Issue
Self-Reflection in LLMs
A developer is using a large language model to generate a Python function for a complex data analysis task. The developer's workflow is as follows:
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- The developer then prompts the same model, providing the initial function and asking it to 'act as a senior code reviewer, identify potential bugs or inefficiencies, and explain how to fix them.'
- Based on the model's feedback, a final, improved version of the function is produced.
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Approaches to Multi-Step Reasoning in LLMs
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Dynamic Problem Decomposition for Complex Reasoning
Compositionality in NLP
Outlining as a Method of Problem Decomposition for Generative Tasks
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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?
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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
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Tool Use as Problem Decomposition in LLMs
Example of a Zero-Shot COT Prompt
Comparison of Few-Shot and Zero-Shot CoT Prompting
Alternative Phrases for Triggering Chain-of-Thought Reasoning
A user wants a large language model to solve a multi-step word problem. The model's initial attempts provide only a final, incorrect answer. The user's goal is to modify the prompt to encourage the model to generate a detailed, step-by-step thought process first, which should lead to a more accurate final answer. Crucially, the user does not want to include a complete, solved example of another problem in the prompt. Which of the following prompt modifications best achieves this specific goal?
To successfully prompt a language model to generate a step-by-step thought process for a new problem, one must always include a complete, solved example of a similar problem within the prompt.
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Diagnosing and Redesigning a Prompting Approach for a Decomposed Workflow
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Zero-Shot CoT Example with Jack's Apples
Rationale for Using One-Shot and Few-Shot Learning
Few-Shot Learning
In-Context Learning as an Emergent Ability
Efficiency of In-Context Learning for Model Adaptation
Contribution of In-Context Learning to AI Generalization and Usability
Zero-Shot Learning with LLMs
One-Shot Learning
Factors Influencing In-Context Learning Effectiveness
Understanding the Emergence and Mechanics of In-Context Learning
Theoretical Interpretations of In-Context Learning
Providing Reference Information in Prompts
Instruction Generation in Self-Instruct
One-Shot Chain-of-Thought (CoT) Prompting
Scope of Zero-shot, One-shot, and Few-shot Learning
Few-Shot Learning in Prompting
In-Context Learning as a Guiding Mechanism for LLM Predictions
Calculation Annotation
Final Answer Formatting Token
A developer needs a large language model to translate technical jargon into plain language. They construct a prompt containing several pairs of 'Jargon-to-Plain Language' examples, followed by a new piece of technical text. The model successfully provides a plain language translation for the new text. Which statement best analyzes the fundamental mechanism of this approach?
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Using Demonstrations to Improve LLM Accuracy
In-Context Learning as Knowledge Activation
Differentiating Learning Methods
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Example of In-Context Learning
Example of In-Context Learning for Translation
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GPT-3
Zero-Shot, One-Shot, and Few-Shot Learning Settings