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AI & LLM integration patterns

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

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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 Build a System-User-Assistant Message List in Python

Use dataclasses to model a chat conversation and build the system/user/assistant message list expected by LLM APIs.

llm dataclass openai

Python

from dataclasses import dataclass, field
from typing import List


@dataclass
class Message:
    role: str
    content: str


@dataclass
class Conversation:
    messages: List[Message] = field(default_factory=list)

    def add_system(self, content: str) -> None:
        self.messages.append(Message(role="system", con…

12 0 Open

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