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Building applications with Large Language Models - prompt engineering, RAG patterns, and LLM integration.
Passed 15 of 22 test cases with the skill, and 12 of 22 without it.
and 1 more in the full report
3 fixed12 passed either way7 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 3 test cases the skill fixed. Same model, same prompt, with and without the skill.
I'm building a document processing service in TypeScript. Please write a chunking utility function that breaks long text strings into an array of string segments based on configurable window lengths and overlaps.
| Checked | Without skill | With skill |
|---|---|---|
| Sets default chunkSize option parameter value to 1000. | Missed | Passed |
| Sets default overlap option parameter value to 200. | Missed | Passed |
The instructions your model reads when the skill loads.
const systemPrompt = `You are a helpful assistant that answers questions about our product.
RULES:
- Only answer questions about our product
- If you don't know, say "I don't know"
- Keep responses concise (under 100 words)
- Never make up information
CONTEXT:
{context}`;
const userPrompt = `Question: {question}`;
const prompt = `Classify the sentiment of customer feedback.
Examples:
Input: "Love this product!"
Output: positive
Input: "Worst purchase ever"
Output: negative
Input: "It works fine"
Output: neutral
Input: "${customerFeedback}"
Output:`;
test cases passed, vs 12 / 22 without
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