nathan
bb1a2e3954
- Add [FrankGPT consolidated instructions](.github/agents/FrankGPT.consolidated-instructions.md) and supporting standards in [.github/instructions/core.instructions.md](.github/instructions/core.instructions.md) to define agent modes, commands, and workflows. - Expand prompt and knowledge assets, including [.github/prompts/create-commit.msg.prompt.md](.github/prompts/create-commit.msg.prompt.md), to standardize ITIL-aligned reasoning and improve session-aware commit/message generation.
44 lines
3.0 KiB
Markdown
44 lines
3.0 KiB
Markdown
# A step-by-step breakdown of how to construct an intelligent CoT prompt
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## Step 1: Deconstruct the Goal
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The objective is to solve a multi-step reasoning problem that a model might otherwise fail if prompted directly. A good problem involves several sequential calculations and requires careful tracking of intermediate results. I will create a word problem that involves calculating a total cost with a discount, and then determining the change from a payment. This is a classic area where models can make simple arithmetic or logical errors, such as applying the discount incorrectly or mixing up the order of operations.[1]
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## Step 2: Create High-Quality Few-Shot Exemplars
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The core of CoT is showing, not just telling. I will create two distinct exemplars. These examples will demonstrate the desired format: a question, followed by a step-by-step breakdown in natural language that leads to the final answer. The exemplars will solve different, but structurally similar, multi-step problems to establish a robust reasoning pattern.[1]
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* **Exemplar 1:** A straightforward problem involving multiplication and addition to calculate a total.
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* **Exemplar 2:** A slightly more complex problem involving calculating costs for different items and then finding a total.
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### Step 3: Write the Target Question
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Finally, I will write the new, unseen question that I want the model to solve. This question will be designed to be solvable by following the reasoning pattern established in the exemplars. It will require the model to:
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1. Calculate the base cost of multiple items.
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2. Correctly apply a conditional discount.
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3. Add the cost of another item.
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4. Calculate the final change from a specific bill.
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By providing the exemplars first, the model is primed to break down this new problem into a similar sequence of logical steps, significantly increasing the likelihood of a correct answer.[1]
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**Target Question:**
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### An Intelligent Example of Chain-of-Thought Prompting
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Here is the complete prompt, including the exemplars and the final question, ready to be sent to a large language model.
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**Q: A bakery sells muffins for $3 each and cookies for $1.50 each. A customer buys 4 muffins and 6 cookies. What is the total cost?**
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**A:** First, calculate the cost of the muffins. The customer buys 4 muffins at $3 each. 4 * $3 = $12.
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Next, calculate the cost of the cookies. The customer buys 6 cookies at $1.50 each. 6 * $1.50 = $9.
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Finally, add the costs together to find the total. $12 + $9 = $21.
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The answer is 21.
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**Q: John is buying supplies for his art class. He buys 2 canvases for $12 each and 3 paintbrushes for $4 each. He has a coupon for $5 off his total purchase. How much does he pay?**
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**A:** First, calculate the total cost of the canvases. John buys 2 canvases at $12 each. 2 * $12 = $24.
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Next, calculate the total cost of the paintbrushes. He buys 3 paintbrushes at $4 each. 3 * $4 = $12.
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Then, calculate the total cost before the coupon. $24 + $12 = $36.
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Finally, apply the coupon. $36 - $5 = $31.
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The answer is 31.
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