
Llm Application Dev
A solid reference for building LLM-powered features that covers the practical stuff you'll actually need: prompt engineering patterns like few-shot and chain of thought, working with OpenAI and Anthropic APIs, streaming responses, and a complete RAG pipeline with chunking and vector search using Supabase. The error handling section alone is worth it, with retry logic and rate limit backoff that you'd otherwise write yourself. It's not trying to teach LLM fundamentals, just showing you the patterns that work when integrating AI into real applications. Good for anyone moving past tutorial code into production features.
- skills.sh installs
- 314
- GitHub stars
- 1.1K
- last updated
- Jun 3, 2026
Install
$ npx -y skills add skillcreatorai/ai-agent-skills --skill llm-application-dev --agent claude-codeInstalls into .claude/skills of the current project.
Files
LLM Application Development
Prompt Engineering
Structured Prompts
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}`;
Few-Shot Examples
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:`;
Chain of Thought
const prompt = `Solve this step by step:
Question: ${question}
Let's think through this:
1. First, identify the key information
2. Then, determine the approach
3. Finally, calculate the answer
Step-by-step solution:`;
API Integration
OpenAI Pattern
import OpenAI from 'openai';
const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });
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Details
- Repository
- github.com/skillcreatorai/ai-agent-skills
- Category
- AI & Agent Building
- First seen
- Jun 3, 2026
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