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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 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 Validate JSON Output Against a Dict Schema in Python

Validate JSON-like data against a simple dict schema with type checking and descriptive error messages using only the Python standard library.

json validation schema

Python

from typing import Dict, Any, List, Union

def validate_json(data: Any, schema: Dict[str, str]) -> List[str]:
    """
    Validate JSON-like data against a simple dict schema.
    Schema format: {field_name: expected_type} where type is one of:
    'str', 'int', 'float', 'bool', 'list', 'dict', 'any'
    Returns list …

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AI & LLM integration patterns easy

How to Validate LLM Output in Python

A beginner-friendly DataValidator class that checks required fields and type constraints on LLM-generated or user JSON data.

validation llm json

Python

import json
from typing import Any, Dict, List, Optional


class DataValidator:
    """Simple helper for validating LLM-generated or user data."""

    def __init__(self, required_fields: List[str], schema: Optional[Dict[str, str]] = None):
        self.required_fields = required_fields
        self.schema = schema or…

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AI & LLM integration patterns easy

How to compute exact match metric in Python

Computes the exact match (EM) metric for LLM outputs by normalizing text and comparing predictions against references.

exact-match metric evaluation

Python

def compute_exact_match(predictions, references):
    def normalize(text):
        import re
        text = text.lower().strip()
        text = re.sub(r'\b(a|an|the)\b', ' ', text)
        text = re.sub(r'[^a-z0-9\s]', '', text)
        text = ' '.join(text.split())
        return text

    matches = sum(1 for pred, r…

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AI & LLM integration patterns easy

How to parse JSON in Python: A Beginner's Guide with Code Examples

This guide shows you how to parse JSON data in Python step by step, with practical code examples and expected outputs.

json parsing dictionary

Python

import json
from typing import Any, Dict, List, Optional


class DataHelper:
    """Beginner-friendly helper for common AI/LLM data tasks."""
    
    def __init__(self, data: Optional[Dict[str, Any]] = None):
        self.data = data or {}
    
    def to_prompt(self, template: str) -> str:
        """Format a prompt…

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AI & LLM integration patterns easy

JSON Mode Prompt Schema Output in Python

Extract a user object to JSON with explicit schema keys, ready for LLM JSON-mode prompts.

json schema llm

Python

import json
from typing import Any, Dict


def extract_user_as_json(user: Dict[str, Any]) -> str:
    """Extract a user object and return it as JSON using explicit schema keys."""
    schema_fields = ("id", "name", "email", "is_active")
    user_subset = {key: user[key] for key in schema_fields if key in user}
    ret…

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