10 Tips to Improve Your Prompting Game: Essential Prompting Tricks for LLMs
🔑 Get an AssemblyAI API key to try LLMs with LeMUR: https://www.assemblyai.com/?utm_sourc...
Remember the early days of Google? Back then, older generations often talked to Google like it was a person, asking questions like “Where will the Olympics take place next time?” or “Who is the president of France?” As the tech-savvy children of the house, we had to guide them to use simpler queries like “weather tomorrow in Istanbul” or “president France”.
How times have changed! Now, we’re re-learning to ask questions in natural language to make the most of modern AI and Large Language Models (LLMs).
In this video, I share 10 essential tips to enhance your everyday prompting skills, helping you get better results from LLMs. And stick around until the end for an eleventh bonus tip that you won't want to miss!
Get an AssemblyAI API key to try LLMs with LeMUR: https://www.assemblyai.com/?utm_sourc...
Chapters:
00:00 Introduction
00:50 Tip 1: Include names of subject-matter experts
01:40 Tip 2: Show emotion in your prompts
01:22 Tip 3: Specify a format for the answer
03:22 Tip 4: Tell the LLM to role-play
03:59 Tip 5: Induce COT
04:48 Tip 6: Use prompt chaining
05:21 Tip 7: Refresh your conversations
05:49 Tip 8: Use online tools
06:06 Tip 9: Ask the LLM to correct itself
06:53 Tip 10: Automate your prompts
07:24 BONUS TIP
07:50 Wrap-up
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What is prompting?
Prompting refers to the process of giving a machine learning model, particularly a language model, specific instructions or queries to generate desired outputs. In the context of natural language processing (NLP), prompts are the inputs provided to models like GPT (Generative Pre-trained Transformer) to produce text, answer questions, or perform specific tasks based on the given instructions.
Why is it important to optimize prompts?
Optimizing prompts is crucial because it directly affects the quality and relevance of the responses generated by language models. Well-crafted prompts can lead to more accurate, coherent, and useful outputs, while poorly constructed prompts may result in vague, irrelevant, or incorrect responses. By optimizing prompts, users can ensure that the model understands the context better and provides outputs that meet their specific needs and expectations, enhancing the overall effectiveness of the interaction with the AI.
What is prompt engineering?
Prompt engineering is the practice of designing and refining prompts to achieve the best possible outputs from language models. It involves experimenting with different ways of phrasing questions, providing context, and structuring instructions to guide the model towards producing the most accurate and relevant responses. Prompt engineering is a critical skill in leveraging the full potential of AI, as it helps users to effectively communicate with models and obtain high-quality results tailored to their specific requirements.
Is it necessary to say please and thank you to an LLM?
While it is not necessary to say "please" and "thank you" to a language model (LLM) for it to understand and respond to your prompts, doing so can still be beneficial in some contexts. Using polite language can make your prompts more natural and human-like, which might help in generating more contextually accurate and nuanced responses, especially when the LLM is designed to process natural language comprehensively. Additionally, maintaining polite language can promote a habit of respectful communication, even when interacting with machines. However, the core functionality of the LLM does not depend on the inclusion of polite phrases.
#MachineLearning #DeepLearning
