A user is trying to get a language model to solve the following word problem: 'Roger has 5 tennis balls. He buys 2 more cans of tennis balls, and each can contains 3 balls. How many tennis balls does he have now?' The model consistently gives an incorrect answer. Describe how you would modify the prompt to demonstrate the intermediate reasoning steps required to arrive at the correct solution, without simply giving the final answer.

Google

Chain-of-Thought (COT) prompting is a method that encourages large language models to explicitly produce intermediate reasoning steps before generating final answers. Built on top of techniques like few-shot prompting, it addresses complex reasoning problems by prompting the model to break down complex tasks into simpler sub-tasks. This explicit decomposition of problem-solving is highly beneficial for generating accurate and interpretable outputs.

Chain-of-Thought (COT) Prompting

Chain-of-thought (COT) prompting has proven to be a highly effective technique for improving performance on challenging reasoning tasks, as demonstrated by its successful application on the GSM8K mathematical reasoning benchmark.

Application of COT Prompting on GSM8K Benchmark

A key technique in advanced prompting, particularly Chain-of-Thought, involves structuring the logical reasoning steps for a given problem. Instead of just showing a problem-answer pair in a demonstration, the intermediate steps of the thought process are explicitly laid out to guide the language model.

Structuring Logical Reasoning Steps for Demonstrations

Zero-shot Chain-of-Thought (COT) is a prompting technique that elicits step-by-step reasoning from a large language model without providing any preliminary examples or intermediate reasoning steps. Instead, it relies on adding a simple instructional trigger to the prompt—such as appending the phrase "Let's think step by step."—which provokes the model to independently generate its own reasoning process to reach the final answer.

Zero-Shot Chain-of-Thought (COT) Prompting

Algebraic calculation problems are frequently used in research literature to demonstrate the capabilities of Chain-of-Thought (CoT) prompting. This type of problem serves as a clear example of how CoT can guide a model through a task by breaking it down into manageable, intermediate steps.

Application of CoT to Algebraic Calculation Problems

Chain-of-Thought (CoT) prompting provides multiple advantages. It enables Large Language Models to break down complex tasks into smaller, sequential steps, a process that reflects human problem-solving. This approach enhances transparency and interpretability, as the model's entire reasoning path is visible, allowing users to understand how a conclusion was formed. This visibility can increase user trust in the model's outputs, a critical factor in fields such as medicine, education, and finance. As a form of in-context learning, CoT is broadly applicable to most pre-trained LLMs without requiring fine-tuning. It also offers an efficient way to adapt models to new problems and can foster creative solutions by encouraging the exploration of diverse reasoning paths.

Benefits of Chain-of-Thought (CoT) Prompting

A potential issue with Zero-Shot Chain-of-Thought prompting, especially when using instructions like 'Let’s think step by step,' is that a Large Language Model might generate a detailed reasoning process but fail to state a clear, final conclusion.

Incomplete Answers from Zero-Shot CoT Prompts

An advanced perspective on Chain-of-Thought (CoT) frames the reasoning process as a search problem. The objective is to find an optimal reasoning path from the problem to the solution among numerous possibilities. Each path is composed of multiple intermediate reasoning steps, or states. For this search to be effective, the space of potential paths must be well-defined and large enough to increase the probability of discovering the optimal result.

Chain-of-Thought as a Search Process

The principles of Chain-of-Thought, where a problem is broken down into intermediate steps, can be adapted for Large Language Model alignment. This is achieved by supervising the model not just on its final output, but on the individual steps it takes during its reasoning process.

Supervising Intermediate Reasoning Steps for LLM Alignment

While Chain-of-Thought (COT) prompting is effective for generating intermediate reasoning steps, its utility is limited when dealing with highly complex problems. Simple, linear COT prompts often fail to produce high-quality solutions for tasks that require more intricate and dynamic reasoning processes.

Limitations of Simple Chain-of-Thought Prompting

In Chain-of-Thought (CoT) prompting, large language models benefit significantly more from demonstrations that include detailed problem-solving steps than from simple question-answer pairs. The fundamental technique for creating a CoT prompt is to incorporate these intermediate reasoning steps, which allows the model to learn the process for deriving an answer, not just the final answer itself.

Creating a CoT Prompt by Incorporating Reasoning Steps

While 'Let's think step by step' is a commonly used instruction to elicit a reasoning process from a Large Language Model in Zero-Shot CoT, other phrases can also be effective. Examples of alternative instructions that can be used to prompt for reasoning include 'Let's think logically' and 'Please show me your thinking steps first'.

Alternative Trigger Phrases for Zero-Shot CoT Prompting

A potential drawback of using trigger phrases like 'Let's think step by step' in Zero-Shot Chain-of-Thought prompting is that the Large Language Model may only generate the reasoning process without providing a final, conclusive answer. This can leave the user with a detailed thought process but no definitive solution to the initial query.

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) is a method that guides a Large Language Model's reasoning by providing it with one or more demonstrations within the prompt. Each demonstration consists of a problem paired with its detailed, step-by-step reasoning process, which teaches the model how to approach similar tasks.

Few-Shot Chain-of-Thought (CoT) Prompting

Despite its effectiveness, Chain-of-Thought (CoT) prompting faces several practical challenges. Key limitations include the difficulty of authoring high-quality reasoning demonstrations for few-shot prompts, the absence of a standard methodology for breaking down problems, and the potential for errors in early reasoning steps to corrupt the final outcome.

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.

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.

Designing a Prompting Workflow for a High-Stakes, Multi-Step Task

You are deploying an LLM inside an internal finance tool that must answer multi-step policy questions (e.g., “Is this expense reimbursable given these 6 policy clauses and these 3 trip details?”). Constraints: (1) you have a strict context window budget, so you can include at most ONE short worked example in the prompt; (2) the model’s first-pass answers are often incomplete (it sometimes explains reasoning but forgets to state a final decision); (3) you need a workflow that is reliable across many different question shapes, not just one template.

Write an essay proposing a concrete prompting workflow that combines: in-context learning (if you use it), chain-of-thought prompting (including whether you would use zero-shot CoT), explicit problem decomposition (including whether you would use a least-to-most progression), and an iterative self-refinement loop. Your answer must (a) explain why your chosen combination fits the constraints, (b) describe the exact sequence of LLM calls or prompt sections you would use, (c) explain how intermediate sub-answers feed into later steps, and (d) specify how you will force a clear final decision despite the risk of incomplete zero-shot CoT outputs. Include at least two tradeoffs you are accepting (e.g., latency vs. accuracy, context budget vs. guidance, error propagation vs. transparency) and how you would mitigate them.

Choosing and Justifying a Prompting Strategy Under Context and Quality Constraints

You are building an internal LLM assistant that helps a finance operations team investigate invoice-payment discrepancies. A typical ticket requires (a) extracting key entities from a messy email thread, (b) reconciling amounts across two systems, (c) identifying the most likely root cause (e.g., duplicate invoice, partial payment, currency conversion, timing), and (d) drafting a customer-facing explanation.

Your first implementation uses a single prompt with the full ticket text and the instruction “Let’s think step by step and give the final answer.” It often produces plausible reasoning but (1) misses a critical sub-check (e.g., currency conversion), (2) sometimes never states a clear final conclusion, and (3) varies in format so downstream automation breaks.

Write an essay that proposes a revised prompting workflow that integrates: in-context learning (demonstrations), explicit problem decomposition, least-to-most sequencing of sub-problems, chain-of-thought prompting (including when you would use zero-shot CoT vs a demonstrated CoT example), and an iterative self-refinement loop. Your answer must explain how these elements work together (not as independent tips), what tradeoffs you are making (e.g., context length, reliability, error propagation), and include at least two concrete prompt snippets (not full prompts) that illustrate (i) how you would elicit structured intermediate reasoning while still forcing a final answer, and (ii) how you would run critique-and-revision in self-refinement without losing the decomposed structure.

Diagnosing and Redesigning a Prompting Approach for a Decomposed Workflow

You are deploying an internal LLM assistant to help procurement analysts decide whether a proposed vendor contract requires (a) a standard review, (b) an enhanced review, or (c) an automatic rejection. The decision depends on multiple interdependent rules (e.g., data types handled, cross-border transfers, subcontractors, and exception clauses). In pilot testing, a single prompt that asks for the final decision often misses a key condition; a zero-shot “Let’s think step by step” prompt sometimes produces a long rationale but forgets to clearly state the final decision; and when you add a few demonstrations, the model becomes more consistent but still makes occasional early-step mistakes that cascade into the wrong outcome.

Design a prompting workflow (you may describe it as a sequence of LLM calls) that uses: (1) in-context learning via demonstrations, (2) explicit problem decomposition in a least-to-most progression, and (3) an iterative self-refinement loop. Your answer must explain how information flows from one step to the next, how you would prevent or detect cascading errors from early steps, and how you would ensure the model always outputs an unambiguous final decision label (a/b/c) even when using chain-of-thought style reasoning.

Stabilizing an LLM Workflow for Multi-Step Policy Compliance Decisions

You are rolling out an internal LLM assistant for Legal Ops to triage vendor contract clauses into three labels: (A) "standard/low risk", (B) "needs legal review", (C) "reject". The assistant must also produce a short justification that can be audited. In a pilot, you observe two recurring failures: (1) on complex clauses, the model gives confident but wrong labels unless it is guided through intermediate reasoning; (2) when you add a simple instruction like "Let’s think step by step," the model often produces a long rationale but sometimes forgets to output a clear final label.

You are not allowed to fine-tune the model. You can only change the prompting workflow and the content placed in the prompt. You have a strict context window budget, so you can include at most ONE worked example in the prompt, and you must keep the number of model calls per clause to no more than 3.

Case: A new clause says: "Vendor may use Customer Data to improve its services and may share Customer Data with affiliates and subcontractors for that purpose. Customer may opt out of data sharing by emailing support within 10 days of signing." Your policy requires: (i) no sharing of Customer Data with third parties for model training/service improvement unless explicitly prohibited or strongly constrained; (ii) opt-out mechanisms are considered insufficient for sensitive data; (iii) subcontractor access must be tightly limited.

Design a prompting workflow (describe the sequence of prompts/calls and what each prompt contains) that uses in-context learning, problem decomposition/least-to-most prompting, chain-of-thought (including zero-shot CoT where appropriate), and self-refinement to reduce both failure modes while staying within the constraints. Your answer must explain how the techniques interact (e.g., why you place a worked example where you do, how you decompose the decision, how you ensure a final label is always produced, and how critique/revision is used without exceeding 3 calls).

Debugging a Multi-Step LLM Workflow for Contract Clause Risk Triage

You are rolling out an internal LLM assistant for SREs to perform root-cause analysis (RCA) from incident artifacts. The assistant receives: (1) a short incident timeline, (2) 30–60 lines of logs, and (3) a list of recent deploys. The desired output is a single RCA summary with: likely cause, supporting evidence, and a recommended next action.

Constraints: You can only include ONE short demonstration in the prompt due to context limits; the assistant must work on novel incidents; and the first pilot showed two failure modes: (a) the model jumps to a plausible cause without checking evidence across artifacts, and (b) when you add “Let’s think step by step,” it sometimes produces a long analysis but forgets to state a clear final RCA.

As the prompt/workflow designer, propose a concrete prompting plan that combines: (i) in-context learning with the single demonstration, (ii) explicit problem decomposition (including a least-to-most style progression of sub-questions), (iii) chain-of-thought elicitation (including how you would use or avoid zero-shot CoT triggers), and (iv) self-refinement (critique + revision) to reduce both failure modes. Your answer must include the exact structure of the prompt(s) you would send (section headings and what each section contains) and explain why each component is placed where it is.

Designing a Robust Prompting Workflow for Multi-Step Root-Cause Analysis with Limited Examples

You’re building an internal LLM assistant to help ...

Your team is rolling out an internal LLM assistant...

You’re leading an internal enablement team buildin...

You’re building an internal LLM workflow to produc...

An example of one-shot Chain-of-Thought (COT) prompting involves providing a language model with a single demonstration of step-by-step reasoning before asking a new question. For instance, a prompt might first present and solve a word problem about Tom's marbles by explicitly detailing each arithmetic step. This single demonstration then guides the model to apply a similar explicit reasoning process when tasked with a subsequent problem, such as calculating how many apples Jack has left.

Example of One-Shot Chain-of-Thought (COT) Prompting

Chain-of-Thought (CoT) methods are highly versatile and can be applied to a wide variety of tasks beyond simple algebraic calculations. Typical problem-solving scenarios where CoT prompting is effectively used include mathematical reasoning, logical reasoning, commonsense reasoning, symbolic reasoning, and code generation.

Problem-Solving Scenarios for Chain-of-Thought Prompting

The self-consistency method provides a criterion for determining the best prediction from a pool of candidates. Because it relies on a fixed prompt and a single model to sample multiple reasoning paths (typically using Chain-of-Thought, or CoT, prompting), it operates as an instance of output ensembling rather than prompt ensembling. It identifies the optimal output by selecting the prediction that most frequently aligns with the other generated answers.

Learn Before

Related