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Co-authored-by: kamranahmedse <4921183+kamranahmedse@users.noreply.github.com>
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# Hallucination
Hallucination in LLMs refers to generating plausible-sounding but factually incorrect or fabricated information. This occurs when models fill knowledge gaps or present uncertain information with apparent certainty. Mitigation techniques include requesting sources, asking for confidence levels, providing context, and always verifying critical information independently.
Hallucination in LLMs refers to generating plausible-sounding but factually incorrect or fabricated information. This occurs when models fill knowledge gaps or present uncertain information with apparent certainty. Mitigation techniques include requesting sources, asking for confidence levels, providing context, and always verifying critical information independently.
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# OpenAI
OpenAI developed influential language models including GPT-3, GPT-4, and o3, setting industry standards for prompt engineering practices. Their API provides access to powerful LLMs with configurable parameters like temperature and max tokens. Many prompt engineering techniques and best practices originated from working with OpenAI systems.
OpenAI developed influential language models including GPT-3, GPT-4, and o3, setting industry standards for prompt engineering practices. Their API provides access to powerful LLMs with configurable parameters like temperature and max tokens. Many prompt engineering techniques and best practices originated from working with OpenAI systems.
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Visit the following resources to learn more:
- [@video@What Is a Prompt Injection Attack?](https://youtu.be/jrHRe9lSqqA?si=6ZN2qrorBDbynFWv)
- [@video@What Is a Prompt Injection Attack?](https://www.youtube.com/watch?v=jrHRe9lSqqA)
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# What is Prompt Engineering?
Prompt engineering is the practice of crafting effective input text to guide AI language models toward desired outputs. It involves designing prompts that communicate intent clearly to get accurate, relevant responses. This iterative process requires understanding how LLMs work as prediction engines and using techniques to optimize their performance for specific tasks.
Visit the following resources to learn more:
- [@video@RAG vs Fine-Tuning vs Prompt Engineering: Optimizing AI Models](https://youtu.be/zYGDpG-pTho?si=yov4dDrcsHBAkey-&t=522)
- [@video@RAG vs Fine-Tuning vs Prompt Engineering: Optimizing AI Models](https://youtu.be/zYGDpG-pTho?si=yov4dDrcsHBAkey-&t=522)