الاتساق الذاتي والمعرفة المُولّدة
طبّق تقنيات تنشئ فيها LLMs مسارات استدلال متعددة وتختار الإجابة الأكثر اتساقًا، أو تولّد معرفة تساعد على الاستدلال.
الاتساق الذاتي والمعرفة المُولّدة درس مجاني في Prompt Engineering & LLM Optimization for Developers على CoddyKit. هذا هو الدرس 2 من أصل 4. يمكنك قراءة الدرس كاملاً أدناه مجاناً — ثم تمرن عليه مباشرة في المتصفح باستخدام محرر أكواد مدمج ومدرس ذكاء اصطناعي متاح 24/7. هذا الدرس جزء من مسار التعلم في Prompt Engineering & LLM Optimization for Developers، وتقدمك يتزامن عبر الويب وتطبيق CoddyKit. تتضمن دورة Prompt Engineering & LLM Optimization for Developers 4 دروس في المجموع.
بعض أجزاء هذا الدرس لم تُترجم بعد وتظهر باللغة الإنجليزية.
Intro: Consistency & Knowledge
Welcome to Lesson 2! In complex problem-solving, Large Language Models (LLMs) can sometimes struggle, leading to incorrect or inconsistent answers.
This lesson introduces two powerful techniques to boost their reliability: Self-Consistency and Generated Knowledge. These methods help LLMs 'think' more deeply and systematically.
Why Advanced Reasoning?
LLMs are great at generating text, but they can sometimes make logical errors or 'hallucinate' (produce factually incorrect information), especially with multi-step reasoning.
Advanced prompting strategies like Self-Consistency and Generated Knowledge aim to:
- Improve accuracy for complex tasks.
- Reduce the likelihood of factual errors.
- Make LLM responses more robust and reliable.
Understanding Self-Consistency
Self-Consistency is a technique where you prompt an LLM to generate multiple distinct reasoning paths or answers for the same question.
Instead of relying on a single output, you then aggregate these different outputs and select the most consistent (e.g., the most frequent) answer. It's like asking several experts and taking the majority opinion.
Self-Consistency in Action
Imagine asking an LLM: "If a train leaves station A at 8 AM traveling at 60 mph, and another leaves station B (300 miles away) at 9 AM traveling at 70 mph, when do they meet?"
A single prompt might give a wrong answer. With self-consistency, you'd ask this multiple times, perhaps with slightly varied phrasing, then compare the results to find the most common meeting time.
Code: Simple Self-Consistency
This Python example simulates calling an LLM multiple times for a math problem. It then picks the most frequent answer, enhancing reliability.
import collections
import random
def call_llm(prompt):
# Simulate LLM responses for a math problem
# In a real app, this would be an actual LLM API call
if "What is (15 * 3) - 7?" in prompt:
return random.choice(["38", "The answer is 38.", "40 (Oops!)"])
return "Simulated response."
def main():
print("--- Self-Consistency Example ---")
question_prompt = "What is (15 * 3) - 7? Give your final numeric answer only."
answers = []
num_attempts = 5
for i in range(num_attempts):
raw_output = call_llm(question_prompt)
# Simple extraction of numeric part
numeric_answer = ''.join(filter(str.isdigit, raw_output))
if numeric_answer:
answers.append(int(numeric_answer))
print(f"Attempt {i+1}: {numeric_answer}")
else:
print(f"Attempt {i+1}: Could not parse '{raw_output}'")
# Find the most common answer (voting)
if answers:
most_common = collections.Counter(answers).most_common(1)
print(f"\nMost consistent answer: {most_common[0][0]}")
else:
print("\nNo valid answers generated.")
if __name__ == "__main__":
main()Introducing Generated Knowledge
Generated Knowledge is a technique where the LLM first generates relevant facts, context, or intermediate thoughts, and then uses this self-generated information to answer the main query.
This is akin to doing research before writing an essay. The LLM essentially 'pre-computes' or 'recalls' relevant knowledge to build a stronger foundation for its final answer.
Generated Knowledge in Practice
Consider a question like: "Describe the main differences between a black hole and a wormhole."
Instead of directly answering, you could first prompt the LLM to:
- "List key characteristics of a black hole."
- "List key characteristics of a wormhole."
Then, use these generated lists as context for the original question, leading to a more informed and accurate comparison.
Code: Pre-computation with LLM
This Python example demonstrates a two-step process: first, asking the LLM to generate knowledge, and then using that knowledge in a subsequent prompt to answer a complex question.
def call_llm(prompt):
# Simulate LLM responses for knowledge generation
if "What are the key components of prompt injection?" in prompt:
return "Malicious user input, LLM vulnerability to new instructions, attempt to bypass security or extract data."
elif "Using the information about Malicious user input, LLM vulnerability to new instructions, attempt to bypass security or extract data., explain the concept of 'prompt injection' in cybersecurity." in prompt:
return "Prompt injection is an attack where crafted malicious user input manipulates an LLM to override its original instructions, potentially leading to unauthorized actions, data exposure, or harmful content generation. It exploits the LLM's tendency to follow new directions even if they contradict its safety guidelines."
return "Simulated response."
def main():
print("--- Generated Knowledge Example ---")
# Step 1: Generate knowledge
knowledge_prompt = "What are the key components of prompt injection?"
print(f"LLM generating knowledge...")
generated_knowledge = call_llm(knowledge_prompt)
print(f"Generated Knowledge:\n{generated_knowledge}\n")
# Step 2: Use generated knowledge to answer the main question
main_question = "explain the concept of 'prompt injection' in cybersecurity."
final_prompt = f"Using the information about {generated_knowledge}, {main_question}"
print(f"LLM answering main question using generated knowledge...")
final_answer = call_llm(final_prompt)
print(f"Final Answer:\n{final_answer}")
if __name__ == "__main__":
main()Synergies: Combining Techniques
While powerful individually, Self-Consistency and Generated Knowledge can also be combined for even greater robustness.
For instance, you could first use Generated Knowledge to create a robust set of facts, and then apply Self-Consistency to the final answer generation phase, ensuring both factual grounding and reliable output.
Check Your Understanding
Test your knowledge about Self-Consistency and Generated Knowledge.
Summary: Powering LLMs
In this lesson, you learned about two advanced prompting strategies:
- Self-Consistency: Generating multiple answers and selecting the most common one to improve reliability.
- Generated Knowledge: Having the LLM create relevant context first, then using that context to answer the main question.
These techniques empower LLMs to tackle complex problems with greater accuracy and less risk of errors. Continue experimenting with them to unlock more robust LLM applications!
تعلم Prompt Engineering & LLM Optimization for Developers مع معلم ذكاء اصطناعي — مجانًا
اكتب وقم بتشغيل أكوادك الفعلية في المتصفح، واحصل على مساعدة فورية من معلم ذكاء اصطناعي متاح 24/7، واستمر من حيث توقفت على الويب أو في التطبيق.
- الدورات
- 12
- الدروس
- 48
الأسئلة الشائعة
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ماذا ستتعلم في «الاتساق الذاتي والمعرفة المُولّدة»؟
طبّق تقنيات تنشئ فيها LLMs مسارات استدلال متعددة وتختار الإجابة الأكثر اتساقًا، أو تولّد معرفة تساعد على الاستدلال. تتمرن على Prompt Engineering & LLM Optimization for Developers مع أكواد عملية تشغلها مباشرة في المتصفح، ومدرس ذكاء اصطناعي متاح 24/7 يجيب على أسئلتك أثناء عملك.
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كم من الوقت يستغرق درس «الاتساق الذاتي والمعرفة المُولّدة»؟
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هل يمكنني كتابة وتشغيل أكواد في درس Prompt Engineering & LLM Optimization for Developers هذا؟
نعم. كل درس في Prompt Engineering & LLM Optimization for Developers يتضمن محرر أكواد مدمج، لذا تكتب وتشغل أكواداً حقيقية مباشرة في متصفحك وتحصل على تعليقات فورية من الذكاء الاصطناعي — بدون إعداد محلي.
جميع الدروس في هذه الدورة
- هندسة المطالبات القائمة على سلسلة الأفكار
- الاتساق الذاتي والمعرفة المُولّدة
- مطالبات شجرة الأفكار والرسوم البيانية
- ReAct: الاستدلال وتنفيذ الإجراءات باستخدام الأدوات