Prompt Engineering Overview

A lecture covering the basics of prompt engineering and all the latest prompt engineering techniques. I also cover tools and applications followed by a conclusion and future directions.

Check out our upcoming live courses to learn more about LLMs:

00:00 Part 1 - Introduction to Prompt Engineering
19:24 Part 2 - Advanced Techniques for Prompt Engineering
40:05 Part 3 - Tools and Applications
51:52 Part 4 - Conclusion and Future Directions

#openai #gpt3 #artificialintelligence #nlp #chatgpt #langchain #machinelearning

Corrections:

[dameanvil]

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.

[markvyber]

amazing video and great resources! Thank you Elvis! <3

[Galaron]

My obsidian notebook on one monitor, this video on the other. Taking notes, thinking things through. Tip of the hat for this fine video.

[LesCalvin]

My friend, where's the flashy thum bnail with screaming/amazed people? Where's the promise of making $500, 000 overnight. WHERE'S YOUR MATRIX SCREENSAVER?

What's that? You don't feel the need to insult your viewers, yourself, or the science by promising the impossible and overemploying hyperbole?

Whatever dude. You just keep on producing the absolute best video I've seen yet on prompt engineering. See if that gets you some kind of amazing career or something.

By the way, that was all sarcasm. Thank you so much for this video!

[hernanperez]

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

[dontwasteachance]

This is the best overview of prompt engineering that I have seen! Thank you!

[skvorets]

25:29, the text is, 'She bought 5 bagels for $3 each. This means she spent 5'. Apart of that - great video, thanks

[hafizmuhammadqasim]

It's very beneficial for learning prompting for beginners. Thank you for your effort.

[ChristianEmenike]

This is going to be a wide spread field and one of the hottest area of interest for businesses

[KasperJunge]

Simply great content, it is sincerely appreciated! Keep up the good work Elvis 💪😎

[hariase]

Prompt: Instructions, Context, Input data, output Indicator

Tasks:
Question Answering
Text Classification
Code Generation
Summarizing
Role playing
Reasoning

[nisarshah]

Thank you Elvis, this one is very useful.
I need this for generating long blog posts.
Any suggestion regarding this use case.
What needs to be done for generating long blog posts.

[verbze]

In the end, prompting seems to be just a higher level programming construct, closer to natural everyday language. Precision still matters somewhat to get the most accurate results, but a whole much less so than your 3/4G languages. Soon, the machine will be so good at understanding context with additional input sensors, it'll almost feel like you can create with thought alone. Exciting times we're living through.

[Davipar]

Thank you soo much for putting all of this together!!

[mathewchan]

just find out there is prompt engineering, thanks for the lecture

[MachineMinds_AI]

Thanks for putting this together Elvis.

[mohamedezzat]

One of the best lecture I have ever attended

[rwang]

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.

[iduomollc]

Fantastic info. Thank you for your hard work.

[ankushsharma]

Thanks for the detailed presentation, really helpful :)