Reference library

AI & LLM integration patterns

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

3 matches

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 easy

How to Parse Chat Completion JSON in Python

Parse a mock OpenAI chat completion JSON response into a clean dictionary with content, finish reason, and model.

json openai chat-completion

Python

import json

def parse_chat_response(raw: str) -> dict:
    data = json.loads(raw)
    choice = data["choices"][0]
    return {
        "content": choice["message"]["content"],
        "finish_reason": choice["finish_reason"],
        "model": data["model"],
    }

if __name__ == "__main__":
    mock_response = '''
  …

14 0 Open

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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