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How can I access this playground? Congrats on the great video!

Whats the autogen course link?

Good

Thank you Elvis! Like you scientific non clickbaitie approach!

keep up the great work!

Thanks for the great content!

GOOD INDIAN ACCENT, VERY UNDERSTANDABLE BEST IS TO READ THE PAPER

I love the format, some things I'm not going to read further but I'm so glad to get a quick summary on them. Keep it going! 👍

7:15 "I notice the formatting is not so great here..." If you click on the button "Add to conversation" below, it will appear formatted correctly in the left panel. (You can then delete it if you want to regenerate the response and/or modify the query.)

You are very good at noticing news that deserves attention! I would like to see a weekly digest of the most important technologies and detailed analyses of some of them during the week.

Thanks for sharing Elvis

Great video👍 A deep dive on xLSTM would be cool😀

Awesome 🙌🏻

Please explain 1-Bit LLM in detail.

Hope you enjoy! Also let me know if you would like me to do a deep dive on any of the topics.

Awesome. can you upload more videos please?

You can do this at 1x or normal frame rate instead. Not able to listen what you say about the paper, no clarity.

Amazing video. Thanks for the concise review of key concepts.

In a way, this feels like optimizing the decoding process during training (think beam search + gradient descent). Very cool paper and very interesting video!

I mentioned this paper in a video that I will post later today, I'll link to this video so people can go deeper. Love your content on Twitter, looking forward to watching your TH-cam stuff too!

Why couldn't you have had all 4 in the example on the first slide of your video? It's a great color mapping for everything else but you neglected to include context.

Heh, Badr is my brother :3 very proud of him. Thnx for the video

Awesome! 😁

Hello @Elvis, Thanks for summarizing this so nicely. I agree with your point that IR system can make it much better to use with small or large language model. I think with this we can use less complex language model and information retrieval to answer more complex questions. I believe that this is what we do ourselves as well, i.e., we use are knowledge whenever it is sufficient but when it is not and we identify those instances we access either external sources of knowledge or those compartmentalized knowledge in our brain to retrieve the information that a question requires to answer. This is a great set to actually make the large language model smaller but effective. Thanks for describing it so well. I am looking forward to the following articles.

Would love that to be a video series of interesting LLM paper summaries!

Em PTBR th-cam.com/video/0VtGC_N3Rvk/w-d-xo.htmlsi=s5QDIeDhtLmYlZxb

1st comment. How to integrate?

Just curious why you decided to use GPT-3.5 Turbo and not any of the other options? :)

Best video thank you.

Let me know if you woud like to see more videos like this and deep dives in the future.

Great pick!

Great finding! I will utilise it7

Please like and comment if you find these short paper summaries useful.

It’s an amazing guide thank you Elvis!

Hi everyone. I am trying out a new series of short summaries of interesting AI papers. Let me know if you like this format. :) Paper link: arxiv.org/abs/2404.03414

Thank you so much. Good video.

Great video - Thank you for putting this together. Quick question: is Data Augmented Generation the same as Retrieval Augmented Generation? They sure seem very similar in concept and implementation.

First of all Great Job!!! It really did help alot... also i request you to post more lessons... Or at least tell when the other chapters will be uploaded...

But could you pls tell why we cannot directly use the playground instead where we can give the prompt in natural language directly and get the response. without using the python code ?

00:41 🔑 Prompt engineering involves using instructions and context to leverage language models effectively for various applications beyond just language tasks. 02:18 🔍 Prompt engineering is crucial for understanding language model capabilities, applicable in research and industry, as highlighted by job postings emphasizing this skill. 03:37 🛠 Components of a prompt include instructions, context, input data, and output indicators, affecting the model's response, with elements like temperature and top P influencing model output diversity. 05:45 📚 Prompt engineering applies to various tasks like text summarization, question answering, text classification, role playing, code generation, and reasoning, showcasing diverse applications. 09:57 💻 Language models, like OpenAI's, exhibit impressive code generation abilities, handling queries from natural language prompts for tasks such as SQL query generation. 10:51 🤔 While language models can reason to an extent, specific prompts and techniques like Chain of Thought prompting aid in improving their reasoning capabilities, although it's an evolving field. 11:19 📝 The lecture delves into code examples and tools, showcasing how prompt engineering techniques are applied practically, using OpenAI's Python client and other tools. 19:34 🚀 Advanced techniques like Few Shot Prompts, Chain of Thought prompting, and Zero Shot Chain of Thought prompting boost performance on complex tasks by providing demonstrations and step-by-step reasoning instructions to the language model. 23:13 🌟 Prompt engineering is an exciting space where crafting clever prompts empowers language models, allowing for powerful capabilities and advancements in various applications. 23:27 🧠 Prompt engineering aims to improve language models for complex reasoning tasks, as these models aren't naturally adept at such tasks. 24:22 🗳 Self-consistency in prompting involves generating multiple diverse reasoning paths and selecting the most consistent answers, boosting performance on tasks like arithmetic and Common Sense reasoning. 25:16 🔍 Demonstrating steps to solve problems within prompts guides models to produce correct answers consistently. 26:37 📚 Using language models to generate knowledge for specific tasks has emerged as a promising technique, even without external sources or APIs. 30:15 🐍 Program-aided language models use interpreters like Python to generate intermediate reasoning steps, enhancing complex problem-solving. 32:35 🔄 React frameworks utilize language models and external sources interchangeably for reasoning traces, action plans, and task handling. 35:20 📊 Tools and platforms for prompt engineering offer capabilities for development, evaluation, versioning, and deployment of prompts. 40:08 🧰 Various tools allow combining language models with external sources or APIs for sophisticated applications, augmenting the generation process. 44:45 📝 Leveraging tools like Long-Chain allows building on language models by chaining and augmenting data for generating responses. 46:22 🧠 Prompt engineering involves combining react-based actions with language models, showcasing the observation, thought, and action sequence for varied tasks. 47:53 🛠 Updated and accurate information from external sources is crucial for prompt engineering applications, highlighting the importance of up-to-date data stores. 48:34 📊 Data augmentation in prompt engineering involves reliance on external sources and tools to generate varied content, requiring data preparation and formatting. 50:34 💬 Prompt engineering explores clever problem-solving techniques to engage language models effectively, like converting questions into different languages while maintaining context and sources. 52:40 ⚠ Model safety is a critical aspect of prompt engineering, focusing on understanding and mitigating language model limitations, biases, and vulnerabilities, including initiatives like prompt injections to identify system vulnerabilities. 55:12 🔒 Potential vulnerabilities like prompt injection, prompt leaking, and jailbreaking highlight risks of manipulating language model outputs, emphasizing the importance of reinforcing system safety measures. 58:30 🎯 Reinforcement Learning from Human Feedback (RLHF) aims to train language models to meet human preferences, emphasizing the relevance of high-quality prompt datasets in this training process. 01:00:06 🌐 Prompt engineering facilitates the integration of external sources into language models, enabling diverse reasoning capabilities and applications, particularly useful for scientific tasks requiring factual references. 01:01:27 🔄 Understanding emerging language model capabilities, such as thought prompting, multi-modality, and graph data handling, is a crucial area for future exploration and development in AI research.

Good Content.👌✌

I am a complete alien on this topic, yet I can see the value of your videos. Great job bro 👏

Awesome

Fantastic info. Thank you for your hard work.

LLM a stupid system that works like a parrot, into which an incredible amount of money has been poured, and now for this money the entire community is trying to revive the corpse with crutches, to make it think what it cannot initially think

Thank you for sharing this video.. it's really grate learning. Can you elaborate prompt engineering for Multiple Choice Question Answering task ?