Prompt engineering is one of the best places to start in this era of AI.
Understanding the core concepts will help you get the most out of generat...
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I need serious feedback guys.
Let me know if you loved this :)
It's a must-read. Developers should know what to search for.
I never realized but I have been using these concepts.
Absolutely, many developers use these concepts intuitively and often don’t realize there's a specific term for them 😅 Anyway, thanks for reading Jitendra!
Amazing article man. I confess that i'm one of those guys that kinda look frown upon "Prompt Engineering", but i'm trying to change my mind about it, because the world is clearly evolving towards AI being a permanent sidekick to software developers, so cudos to you for the amazing article, definitely helped change my view towards the subject! Bookmarking it to revisit it later!
Thanks for reading Naftaly 🙌
Trust me, you're not alone. Even I thought it was unnecessary a while back, but I'm confident that everyone should at least be aware of these little concepts. They're not complex and can definitely make our lives so much easier :)
Amazingly detailed article!
The best thing is I am using most of the described techniques/concepts from the beginning. Maybe it is kind of an intuition many of us developers have more or less.
Great article. The way you broke it down diving deep into using constraints, RAG, Chain of thought, really deserves appreciation.
I rate this as high quality.
Thanks, I also realized it very late.
Developers use these techniques on intuition but aren't even aware of the term, yk!
To ask the model to adopt a specific persona, simply provide clear instructions about the characteristics, tone, or style you want it to emulate. For example:
"Please respond as if you are a professional financial advisor with a formal tone."
Or:
"Adopt the persona of a friendly and enthusiastic travel guide, offering tips with excitement and warmth."
This helps the model tailor its responses to match the desired persona.
Thanks for sharing your opinion with an example, but why are you posting multiple comments 😅
Great article! Thanks for sharing!
I'm glad you loved reading this 🙌
Very nice write up and useful tips, thanks for sharing.
I had not thought about the concept of prompt injection until now.
Yes, it's especially important when you're using LLMs in any product for specific tasks that involve user prompts. Users can ask to entirely ignore previous inputs, which could lead to disasters. Thank you for reading 🙌
Thank you for this very helpful post. I will try it this week!!
Thanks for reading Helen 🙌
Let me know how it went and if you face any problems.
"Act like a terminal" made my day :)
This article is awesome and very thorough. It earned you a follow from me. :D
Great job, keep writing more like this!
This is a really great resource! I found this because I actually have a question myself haha!
Imagine you have a question, you search for something and in return you learn about so many new concepts. Curiosity is so cool :)
Thanks for reading!
Good post,enjoyed reading it,it simplifies understanding of prompt engineering.
Thanks, I tried to put in my best effort :)
Wow. I love this. I am currently taking a course on prompt engineering and your article serves as a guide. Kudos!
Oh, thank you!
By the way, what course do you recommend for prompt engineering :)
Thanks for your suggestions, I really appreciate it and I am working with RAG, it really helpful for me.
Refining your prompts is crucial as there isn't a one-size-fits-all approach. Start with a clear and specific query, then adjust based on the responses you receive. Experiment with different phrasings and details to hone in on the most effective way to communicate your needs. Continuously iterate and learn from the outcomes to improve the accuracy and relevance of the responses. Flexibility and experimentation are key to achieving better results.
Pause and Reflect: Allow a brief moment for the model to generate a more thoughtful and accurate response.
Clarify and Expand: Provide additional context or clarify any ambiguities to assist the model in understanding your request better.
Patience: Understand that complex queries may require more time to process thoroughly.
Iterate: If the response isn't satisfactory, ask follow-up questions to refine the output.
Feedback: Offer feedback on the response to help improve the model's future performance.