Reference library

AI & LLM integration patterns

Call LLM APIs, structure prompts, parse responses, and ship AI features safely.

5 matches

Chain of Thought Prompting in Python: Step-by-Step Reasoning Demo

This demo shows how to structure a function that explains its own reasoning step-by-step, mimicking chain-of-thought prompting for AI systems.

ai llm reasoning

Python

def solve_math_step_by_step(expression: str) -> str:
    """Solves a simple expression, showing each reasoning step."""
    # Step 1: Parse the expression (assume "a + b" or "a - b")
    parts = expression.split()
    a = int(parts[0])
    op = parts[1]
    b = int(parts[2])
    
    steps = []
    steps.append(f"Step…

16 0 Open

AI & LLM integration patterns easy

Demonstrate Prompt Injection Bypass in Python

Simulate why naive system prompt filters fail against prompt injection with casing and spacing variations.

prompt-injection llm-security demo

Python

# Demonstrate why system prompts can be bypassed by simulated user input
# This demo shows a naive filter being ignored via prompt injection

def process_user_message(message, system_rules):
    """Simulate an AI that follows system rules but gets tricked."""
    # Claim to check system rules
    for rule in system_ru…

14 0 Open

AI & LLM integration patterns easy

How to Convert Data to JSON and Back in Python

Convert a Python dict into a JSON string with indentation, then parse it back into a dict, demonstrating a common round-trip conversion for beginners.

json serialization conversion

Python

import json
from datetime import datetime

def convert_data(data):
    """Convert a dict into a JSON string and back to dict."""
    json_str = json.dumps(data, indent=2)
    parsed = json.loads(json_str)
    return json_str, parsed

def main():
    sample_data = {
        "user": "alice",
        "message": "hello",
…

11 0 Open

AI & LLM integration patterns medium

How to Retry LLM Calls on Rate Limit Errors in Python

Implement a retry mechanism with exponential backoff for LLM API calls that raises a custom RateLimitError, using a mock function to demonstrate the pattern.

llm retry rate-limit

Python

import time
import random


def mock_llm_call():
    """Simulates an LLM API call that may raise a rate limit error."""
    if random.random() < 0.4:  # 40% chance of rate limit
        raise RateLimitError("Rate limit exceeded. Try again later.")
    return {"response": "Hello world from mock LLM"}


class RateLimitE…

16 0 Open

AI & LLM integration patterns medium

How to implement exponential backoff for LLM API calls in Python

A decorator that retries flaky LLM API calls with exponential delay, using a mock client to demonstrate the pattern.

exponential-backoff retries llm

Python

import time
import random

class MockLLM:
    def call(self, prompt):
        if random.random() < 0.7:  # 70% chance of transient failure
            raise ConnectionError("API unavailable")
        return f"LLM response for: {prompt}"

def with_exponential_backoff(max_retries=5, base_delay=0.1):
    def decorator(fu…

14 0 Open

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