Reference library

AI & LLM integration patterns

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

4 matches

How to Build a Zero-Shot Classification Prompt in Python

Creates a prompt for zero-shot text classification by pairing input text with candidate labels and a hypothesis template.

zero-shot prompt classification

Python

from typing import Dict, List


def build_zero_shot_prompt(
    text: str,
    candidate_labels: List[str],
    hypothesis_template: str = "This is about {}.",
) -> Dict[str, List[str]]:
    """Build a prompt ready for zero-shot classification."""
    return {
        "sequences": text,
        "candidate_labels": can…

13 0 Open

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 …

13 0 Open

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…

14 0 Open

AI & LLM integration patterns easy

How to compute ROUGE recall in Python

Compute ROUGE recall by counting token overlap between a reference and candidate summary with pure Python.

rouge nlp evaluation

Python

def rouge_recall(reference, candidate):
    ref_tokens = reference.lower().split()
    cand_tokens = candidate.lower().split()

    ref_counts = {}
    for token in ref_tokens:
        ref_counts[token] = ref_counts.get(token, 0) + 1

    cand_counts = {}
    for token in cand_tokens:
        cand_counts[token] = cand…

12 0 Open

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