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AI & LLM integration patterns

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

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How to Filter Toxic Keywords in Python

Filter toxic keywords from text by replacing each occurrence with asterisks, useful as a basic guardrail for LLM inputs.

guardrails text-filtering llm-safety

Python

TOXIC_KEYWORDS = ["insult", "threat", "hate", "violence", "spam"]


def guardrails_filter(text: str, keywords: list[str] | None = None) -> str:
    """Filter out toxic keywords from the given text.

    Args:
        text: The input text to filter.
        keywords: Optional keyword list. Defaults to TOXIC_KEYWORDS.

…

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AI & LLM integration patterns easy

How to Parse JSON from LLM Model Output Fence in Python

Extract and parse a JSON object from a language model's output that may be wrapped in triple-backtick fences with an optional language tag.

json llm parsing

Python

import json
import re

def parse_json_from_fence(text):
    """
    Extract JSON object from a model output that may be wrapped in
    triple-backtick fences with optional language tag.
    """
    # Match content inside

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AI & LLM integration patterns easy

How to Parse an LLM Response in Python

This code parses a JSON string from an LLM response, stripping code fences and handling common issues like whitespace, returning a Python dictionary.

llm json parsing

Python

import json
from typing import Any, Dict, List


def parse_llm_response(response: str) -> Dict[str, Any]:
    """Parse a JSON string from an LLM response, handling common edge cases."""
    # Remove code fences if present
    cleaned = response.strip()
    if cleaned.startswith("

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AI & LLM integration patterns easy

How to Render a Jinja-like Template from a Dict in Python

Replace {{placeholders}} in a string using values from a Python dict with a simple regex-based template renderer.

templating regex strings

Python

import re

def render_template(template, context):
    pattern = re.compile(r"\{\{\s*(\w+)\s*\}\}")
    def replace(match):
        key = match.group(1)
        return str(context.get(key, ""))
    return pattern.sub(replace, template)

if __name__ == "__main__":
    template = "Hello {{name}}, you have {{count}} new …

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AI & LLM integration patterns easy

How to Stream Tokens from a Mock LLM in Python

Simulate real-time LLM streaming by yielding tokens one at a time with a delay, making it easy to test streaming UIs.

generator llm streaming

Python

import time
from typing import Generator


def stream_tokens(text: str, delay: float = 0.05) -> Generator[str, None, None]:
    """Simulate an LLM streaming tokens word by word."""
    for word in text.split():
        yield word
        time.sleep(delay)


if __name__ == "__main__":
    sample = "Hello world! This is…

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AI & LLM integration patterns easy

How to randomly assign a prompt variant to each key in Python

Randomly pick one variant from a list for each prompt key, useful for A/B testing message variations.

random dictionary a/b-testing

Python

import random

def assign_prompt_variant(prompts: dict[str, list[str]]) -> dict[str, str]:
    """Assign a random prompt variant to each prompt key."""
    return {key: random.choice(variants) for key, variants in prompts.items()}

if __name__ == "__main__":
    prompt_bank = {
        "greeting": ["Hello!", "Hi there…

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