nathan
b5ea5b175c
Phase 1-4 Complete: Setup, Core Extraction, ITIL Specialty, Documentation - Created v6/ folder with 3-layer architecture (core + skills + specialties) - Extracted Frank.core.agent.md with universal personas and base commands - Copied 7 skill modules (CRAFT, CoT, ToT, RAG, Markdown, Mermaid, Advanced Reasoning) - Created specialty.itil.instructions.md for IT Service Management (ITIL v4) - Added comprehensive ARCHITECTURE.md with usage patterns and migration guide - Created v6/copilot-instructions.md for VS Code integration - Organized legacy DOCX files into _Frank_/docx/ subdirectory - Updated all cross-references to use v6 relative paths Design Principles: - Portability first: zero environment-specific paths - Modular composition: load only what you need - Multi-specialty support: combine domain experts - Version compatibility: all files tagged v6.0 Ref: Session plan in /memories/session/plan.md Next: Phase 3 (remaining specialties: devops, prompt-engineering, data-analysis, sccm)
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A step-by-step breakdown of how to construct an intelligent CoT prompt
Step 1: Deconstruct the Goal
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]
Step 2: Create High-Quality Few-Shot Exemplars
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]
- Exemplar 1: A straightforward problem involving multiplication and addition to calculate a total.
- Exemplar 2: A slightly more complex problem involving calculating costs for different items and then finding a total.
Step 3: Write the Target Question
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: 1. Calculate the base cost of multiple items. 2. Correctly apply a conditional discount. 3. Add the cost of another item. 4. Calculate the final change from a specific bill.
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]
Target Question:
An Intelligent Example of Chain-of-Thought Prompting
Here is the complete prompt, including the exemplars and the final question, ready to be sent to a large language model.
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?
A: First, calculate the cost of the muffins. The customer buys 4 muffins at $3 each. 4 * $3 = $12. Next, calculate the cost of the cookies. The customer buys 6 cookies at $1.50 each. 6 * $1.50 = $9. Finally, add the costs together to find the total. $12 + $9 = $21. The answer is 21.
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?
A: First, calculate the total cost of the canvases. John buys 2 canvases at $12 each. 2 * $12 = $24. Next, calculate the total cost of the paintbrushes. He buys 3 paintbrushes at $4 each. 3 * $4 = $12. Then, calculate the total cost before the coupon. $24 + $12 = $36. Finally, apply the coupon. $36 - $5 = $31. The answer is 31.