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AI & LLM integration patterns

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

4 matches

How to Detect Prompt Injection in Python

Implements a regex-based heuristic in Python to flag common prompt injection attempts before sending input to an LLM.

import re

def contains_prompt_injection(user_input: str) -> bool:
    # Directives to ignore previous instructions or act as system
    ignore_patterns = [
        r"\bignore\s+(all\s+)?previous\s+instructions\b",
        r"\bdisregard\s+(all\s+)?previous\s+instructions\b",
        r"\bdon'?t\s+follow\s+(any\s+)?inst…

13 0 Open

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

12 0 Open

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 …

14 0 Open

AI & LLM integration patterns medium

Parse ReAct Logs into Thought Action Observation Steps in Python

Parse a ReAct agent's textual log into structured steps with thought, action, and observation using regex and named tuples.

react regex llm

Python

import re
from collections import namedtuple


ReActStep = namedtuple("ReActStep", ["thought", "action", "observation"])


def parse_react_log(log: str) -> list[ReActStep]:
    """Parse a ReAct log into structured thought/action/observation steps."""
    pattern = re.compile(
        r"Thought:\s*(?P<thought>.+?)\s*"
…

13 0 Open

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