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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…

17 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

AI & LLM integration patterns easy

Serialize and Format Data for LLM Prompts in Python

Use dataclasses and the json module to convert Python objects to JSON strings, parse them back, and format structured data into prompt-friendly text for LLM calls.

dataclasses json llm

Python

import json
from dataclasses import dataclass, asdict


@dataclass
class Recipe:
    """Simple data model to represent a recipe."""
    name: str
    cuisine: str
    prep_minutes: int


def to_json(recipe: Recipe) -> str:
    """Serialize a Recipe to a JSON string."""
    return json.dumps(asdict(recipe), indent=2)

…

14 0 Open

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