Advanced Prompt Engineering: OpenAI Hackathon

Prompt engineering is a novel technique aimed at enhancing AI capabilities by designing effective prompts to guide AI models in producing desired outcomes. This approach requires deep understanding of model architecture, knowledge of potential biases, and insights into information processing. By optimizing prompts, researchers can harness the strengths of AI models and mitigate their limitations. The impact of prompt engineering is evident in various AI applications, such as language translation and sentiment analysis. Overall, it offers a valuable framework for creating more effective AI systems, leading to a more efficient and intelligent future.
In this session we will cover different tips and techniques of advanced prompt engineering, which will help you to build a LLM application on top of Azure OpenAI
learn.microsoft.com/en-us/azu...
learn.microsoft.com/en-us/azu...
platform.openai.com/docs/guid...
www.deeplearning.ai/short-cou...
www.promptengineering.org/
learnprompting.org/docs/intro
www.promptingguide.ai/

[@TagHearty]

🎯 Key Takeaways for quick navigation:
00:54 🔍 Prompt engineering involves designing and refining prompts to communicate effectively with AI language models, making English a crucial programming language for it.
03:14 🔄 Prompt engineering is an iterative process where prompts are refined over time to achieve better results.
05:21 📝 Prompts typically consist of instructions, examples, input data, and output indicators. Context can also be important in some cases.
09:53 🚧 Use delimiters to clearly separate parts of the input in prompts, ensuring clear and specific instructions.
12:07 📋 Prompts should ask the model to produce structured outputs, specifying the desired format like JSON, making post-processing more efficient.
25:57 🧠 Advanced prompt engineering involves designing prompts to guide AI models effectively.
26:52 🤖 When using prompt engineering, instruct the model to work out its own solutions before evaluating others.
30:16 🔍 Few-shot prompting provides examples to help models give better performance on specific tasks.
32:04 🤯 Chain of Thought prompts guide the model to think step by step, aiding in reasoning tasks.
36:13 🧠 Self-consistency involves generating diverse samples and selecting the most consistent answer to improve AI output.
50:38 🧠 The "react" approach involves generating both the reason and action in an iterative manner to reach a final result, allowing external tools to interact.
52:29 🔍 The "rap" approach combines a retriever-based system and a generator-based system to answer questions efficiently.
59:06 🔄 Fine-tuning specializes a model for specific tasks, while "rap" allows models to answer questions from a knowledge base without altering the model.
01:08:22 📚 Retrieval augmented generation (RAG) involves generating knowledge from documents and then using a retriever to find and generate answers to questions.
01:10:09 🧾 RAG provides contextual answers and citations, making it suitable for answering questions from specific datasets.
01:13:24 🧠 Advanced prompts in AI models can provide precise answers with explanations.
01:16:13 📚 RAG (Retrieval-Augmented Generation) can help AI models access external knowledge sources.
01:18:27 🔄 Reactive agents can use search and calculation tools to answer complex questions.
01:19:47 🚧 Consider prompt safety to protect against injection, jailbreaking, and other security risks in AI prompts.
01:21:07 📖 Resources for learning prompt engineering include Microsoft's best practices, openAI's guide, and courses like Deep Learning AI's.
Made with HARPA AI

[@praveenmodi]

Great content, Amit! Thank for sharing your knowledge with the community.

[@urinater]

What about “Dynamic Prompts”?

[@sapito169]

inremeber not only chinise have asian level
indians also have asian level in informatics

[@gmfPimp]

First of all, replace the battery in your fire alarm, whose ever continue to beep. Very distracting.
Other than that, the verbal presentation needs focusing. It wanders without ant type of specifics and even presents a false narrative.
With all things being then same, including using the same seed, a prompt is final. Period. Younrunn it 1 million time, you get the same response.
The part that I find ironic is he reads a definition about "effectively communication"....missed the boat on that one. Seacrest out!