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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 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

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 Render a Jinja-like Template from a Dict in Python

Replace {{placeholders}} in a string using values from a Python dict with a simple regex-based template renderer.

templating regex strings

Python

import re

def render_template(template, context):
    pattern = re.compile(r"\{\{\s*(\w+)\s*\}\}")
    def replace(match):
        key = match.group(1)
        return str(context.get(key, ""))
    return pattern.sub(replace, template)

if __name__ == "__main__":
    template = "Hello {{name}}, you have {{count}} new …

14 0 Open

AI & LLM integration patterns easy

How to build a mock RAG pipeline in Python

Build a minimal Retrieval-Augmented Generation pipeline that retrieves the best-matching document by keyword overlap and generates a template-based answer.

rag llm retrieval

Python

def simple_rag_pipeline(question, documents):
    """
    A minimal mock RAG pipeline: retrieve relevant context, then generate an answer.
    """
    # Step 1: Retrieve — mock retrieval by simple keyword scoring
    scores = []
    for doc in documents:
        doc_words = set(doc.lower().split())
        question_wo…

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