LLM Integration
Integrate Large Language Models with production-grade reliability.
When to Use This Skill
Invoke this skill when:
- •Connecting to Claude, OpenAI, or other LLM APIs
- •Designing effective prompts and system messages
- •Optimizing token usage and costs
- •Implementing streaming responses
Parameter Schema
| Parameter | Type | Required | Description | Default |
|---|---|---|---|---|
provider | enum | Yes | anthropic, openai, google, local | - |
task | string | Yes | Integration goal | - |
streaming | bool | No | Enable streaming | true |
max_tokens | int | No | Response token limit | 4096 |
Quick Start
python
# Anthropic Claude
from anthropic import Anthropic
client = Anthropic()
response = client.messages.create(
model="claude-sonnet-4-20250514",
max_tokens=1024,
messages=[{"role": "user", "content": "Hello!"}]
)
# OpenAI
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
model="gpt-4-turbo",
messages=[{"role": "user", "content": "Hello!"}]
)
Prompt Templates
System Prompt
python
SYSTEM = """You are {role}, an expert in {domain}.
Your task: {task}
Constraints: {constraints}
Output format: {format}"""
Chain-of-Thought
python
COT = """Think step by step: 1. Understand the problem 2. Break it down 3. Solve each part 4. Combine results"""
Cost Optimization
| Model | Input $/1M | Output $/1M | Best For |
|---|---|---|---|
| Claude Haiku | $0.25 | $1.25 | High volume |
| Claude Sonnet | $3 | $15 | Complex tasks |
| Claude Opus | $15 | $75 | Most demanding |
Troubleshooting
| Issue | Solution |
|---|---|
| 429 Rate Limited | Exponential backoff |
| Context overflow | Truncate/summarize |
| Poor output quality | Add examples, lower temp |
| High costs | Use cheaper model, cache |
Best Practices
- •Always implement retry with backoff
- •Use streaming for better UX
- •Cache repeated queries
- •Monitor token usage
Related Skills
- •
ai-agent-basics- Agent architecture - •
rag-systems- Retrieval augmentation - •
tool-calling- Function calling