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'''Prompt engineering''' is the process of structuring an instruction that can be interpreted and understood by a [[generative AI]] model.<ref name="diab">{{Cite web|url=https://cdn.openart.ai/assets/Stable%20Diffusion%20Prompt%20Book%20From%20OpenArt%2011-13.pdf |title=Stable Diffusion Prompt Book |last1=Diab |first1=Mohamad |last2=  Herrera |first2=Julian |last3=Chernow |first3=Bob |date=2022-10-28 |access-date=2023-08-07 |quote="Prompt engineering is the process of structuring words that can be interpreted and understood by a ''text-to-image'' model. Think of it as the language you need to speak in order to tell an AI model what to draw."}}</ref><ref>{{Cite web |author=Ziegler |first1=Albert |last2=Berryman |first2=John |date=17 July 2023 |title=A developer's guide to prompt engineering and LLMs |url=https://github.blog/2023-07-17-prompt-engineering-guide-generative-ai-llms/ |website=The GitHub Blog |quote="Prompt engineering is the art of communicating with a generative AI model."}}</ref> 
A prompt is [[n

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# Contributor License Agreement
Thank you for your interest in making a Contribution to a project of Deepset GmbH, a German company having its registered office at Hermannstraße 168, 12051 Berlin, Germany (**Deepset** or **we**).

In order to clarify the intellectual property license granted with Contributions from any natural person or legal entity, Deepset must have a Contributor License Agreement (**CLA**) on file. The CLA is agreed between each Contributor and Deepset, indicating agreement to the license terms below. This license **is for your protection as a Contributor** (by a disclaimer of liability and aclause designed to protect you in case of a patent dispute) as well as the protection of Deepset and its users; **it does not change your rights to use your own Contributions** for any other purpose.

You accept and agree to the following terms and conditions for your past, present and future Contributions submitted to Deepset.
## Definitions:
**You** (or **Your**) shall mean the natural person or leg

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import argparse
import os
import sys
import re

from pathlib import Path


TARGETS = (
    'latest',

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int ByteReceived;
int i;

void setup() {
  // initialize digital pin LED_BUILTIN as an output.
  pinMode(LED_BUILTIN, OUTPUT);
  Serial.begin(9600);
  i = 0;
}

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# (C) Datadog, Inc. 2018
# All rights reserved
# Licensed under a 3-clause BSD style license (see LICENSE)
import re

import win32service
from six import iteritems

from datadog_checks.checks import AgentCheck

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def pattern_filter(items, whitelist=None, blacklist=None, key=None):
    """This filters `items` by a regular expression `whitelist` and/or
    `blacklist`, with the `whitelist` taking precedence. An optional `key`
    function can be provided that will be passed each item.
    When you have only one type of list, consider using `pattern_whitelist`
    or `pattern_blacklist` for increased performance.
    """
    if (whitelist and blacklist):
      _pattern_filter_chain(items, whitelist, blacklist, key)
    elif whitelist:

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from __future__ import print_function
import json
import subprocess
import argparse
import sys


def search_files(repo, data):
    positives = 0
    for entry in data:

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""" Collect status information for Windows services
"""
# project
from checks import AgentCheck
from checks.wmi_check import WinWMICheck
from utils.containers import hash_mutable
from utils.timeout import TimeoutException


class WindowsService(WinWMICheck):

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# (C) Datadog, Inc. 2010-2016
# All rights reserved
# Licensed under Simplified BSD License (see LICENSE)

'''
Redis checks
'''
# stdlib
from collections import defaultdict
import re

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# (C) Datadog, Inc. 2010-2016
# All rights reserved
# Licensed under Simplified BSD License (see LICENSE)

# stdlib
from datetime import datetime, timedelta
from hashlib import md5
from Queue import Empty, Queue
import re
import ssl