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AI & LLM integration patterns

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

3 matches

How to Build an Agent Loop with Plan, Act, Observe in Python

Implements a simple plan-act-observe loop that an AI agent uses to iteratively complete a task in an environment while storing observations in memory.

agents loop llm

Python

class Agent:
    def __init__(self, name):
        self.name = name
        self.memory = {}

    def plan(self, task):
        return f"Plan for {task}: step 1, step 2, step 3"

    def act(self, plan, environment):
        return f"Executing {plan} in {environment}"

    def observe(self, action_result):
        sel…

18 0 Open

AI & LLM integration patterns easy

How to Create a Mock LLM Judge Rubric Score in Python

Scores a response against a rubric by counting keyword matches, returning total, percentage, and per-criterion feedback.

llm evaluation rubric

Python

def judge_score(response, rubric):
    """Mock LLM judge that scores a response against a rubric."""
    total = 0
    max_total = 0
    feedback = []

    for criterion, rubric_item in rubric.items():
        max_points = rubric_item["max"]
        description = rubric_item["description"]

        # Simple mock scori…

15 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

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