Reference library

AI & LLM integration patterns

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

4 matches

How to Append Few-Shot Examples to a Prompt in Python

This code builds a complete LLM prompt by appending few-shot examples in alternating user/assistant format using a simple loop.

prompt-engineering few-shot llm

Python

def append_few_shot_examples(prompt: str, examples: list[tuple[str, str]], separator: str = "\n\n") -> str:
    """Append few-shot examples to a prompt in alternating user/assistant format."""
    full_prompt = prompt
    for user_input, assistant_output in examples:
        full_prompt = f"{full_prompt}{separator}Use…

15 0 Open

AI & LLM integration patterns medium

How to Build a Data Helper for LLM Prompts in Python

A beginner-friendly helper class that flattens nested dictionaries, formats prompt templates, and safely parses JSON for AI/LLM pipelines.

llm prompt-engineering data-prep

Python

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


class DataHelper:
    """Simple helper class for working with data in AI/LLM pipelines."""
    
    def __init__(self, data: Optional[Dict[str, Any]] = None) -> None:
        self.data = data or {}
    
    def flatten(self, prefix: str = "") -> Dict[str, Any]…

18 0 Open

AI & LLM integration patterns easy

How to Build a Prompt Template with Variable Slots in Python

Create a reusable LLM prompt template with named variable slots using Python's string.Template class and fill them with render() calls.

llm prompt-engineering templates

Python

from string import Template


class PromptTemplate:
    def __init__(self, template_text):
        self.template = Template(template_text)

    def render(self, **kwargs):
        return self.template.substitute(**kwargs)


if __name__ == "__main__":
    template = PromptTemplate(
        "You are a helpful assistant …

14 0 Open

AI & LLM integration patterns easy

Prepare LLM prompt data with a Python helper class

A beginner-friendly Python class that collects records, converts them to JSON, and produces a quick summary for building LLM prompt context.

llm json prompt-engineering

Python

import json
from typing import Any, Dict, List

class DataHelper:
    """Simple helper to prepare data for LLM prompts."""
    
    def __init__(self):
        self.data = []
    
    def add(self, item: Dict[str, Any]) -> "DataHelper":
        self.data.append(item)
        return self
    
    def to_json(self) -> s…

16 0 Open

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