Prompt Engineering and RAG (Retrieval-Augmented Generation)
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- Curriculum
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Unleash your AI’s full potential with our comprehensive course on prompt engineering and RAG (Retrieval-Augmented Generation), and master the art of crafting precise, powerful queries that will transform your language model’s performance.
AI tools can make you a superhuman if you use them correctly. In this course, we will see the theoretical and practical aspects of how to craft special requests for AI in order to bypass its limitations.
Why is this course relevant?
AI tools will automate many tasks in our day to day lives. The labour market will differentiate between the people using AI and the ones who don’t, since the productivity gap between the two categories will deepen as the tools will become more sophisticated.
With prompt engineering, you can position yourself ahead of the curve, gaining a competitive advantage in your professional and personal life. As AI becomes increasingly integrated into various industries, those who can effectively harness its capabilities will be highly sought after. This course will equip you with the necessary skills to excel in this evolving landscape.
What will you learn in this course?
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Foundational Concepts: Understand the basics of AI, language models, and how they work, so you can have an informed approach to prompt engineering.
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Prompt Engineering Techniques: Discover various strategies and techniques to craft effective prompts that maximize AI performance, including specificity, context, and iterative prompting.
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Customization and Optimization: Learn how to fine-tune prompts to suit specific tasks, industries, or applications, and optimize them for desired outcomes.
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RAG: Learn how to create retrieval-augmented generation tools with Llama and Gemini models.
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Bias Mitigation: Gain insights into AI biases and how to mitigate their impact while crafting prompts, ensuring your AI-driven solutions are fair and unbiased.
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Real-World Applications: Explore practical case studies and real-world scenarios to see how prompt engineering can enhance productivity and efficiency across various domains.
Who is this course for?
This course is designed for professionals, students, and enthusiasts from various fields who want to leverage AI’s potential to improve their work or personal lives. No prior experience in AI or programming is required, as the course is structured to cater to both beginners and advanced learners.
By the end of this course, you will have a solid understanding of prompt engineering and be equipped to use AI tools more effectively, making you an invaluable asset in the rapidly evolving AI-driven world.
Get ready to become a master in using the GPT models, or any large language model out there. With the prompting strategies that you will see in this course, you can easily perform the role of a software engineer, lawyer, social media star or even mathematician. All with the help of ChatGPT and other similar tools.
This course is intended for beginners and experienced tech-savvy people. You don’t need programming experience, however, if you do code, buckle up as we are also going to build a chatbot mobile app using ReactNative and API behind ChatGPT. If not, don’t worry! The course will teach you anything you need to know.
Elevate your AI experience by learning innovative techniques to generate insightful, accurate, and bias-free responses, propelling your projects to new heights of success.
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Introduction -
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Creating a Simple ChatBot App with Vercel AI SDK
Learn what it takes to build a very simple chatbot web application using Vercel AI SDK.
Link to the GitHub repo: https://github.com/LaurentiuGabriel/hugging-face-api
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Making a Social Media Expert ChatBot
Add a purpose to the chatbot using prompt engineering, specifically using role prompting.
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Adding Styles to the ChatBot
Learn how to improve the style of the chatbot web app.
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Introduction to Image Prompting Techniques
An introduction on how to harness image generation AI models.
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Style Modifiers and Quality Boosters
Find our how to improve your image prompting techniques through style modifiers and quality boosters.
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Repetitions and Weighted Terms
How to create better images through repetitions and weighted terms.
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Fixing Deformations and Shot Tyoes
Learn how to fix deformities in the generated pictures and how to leverage shot types in stable diffusion.
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Tips for Midjourney
Learn how to use Midjourney efficiently and effectively.
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Useful ResourcesA list of curated resources for delving deeper into image prompt engineering.
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Reliability and Debiasing
Let's see how we can remove the bias of Large Language Models.
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Prompt Ensembling
Prompt ensembling is a technique where multiple prompts are used to obtain diverse responses from a model, which are then combined to produce a more accurate or robust answer.
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Self Evaluation
Self-evaluation prompting is about asking the model to generate content and then evaluate or grade its own work.
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Introduction to Prompt Hacking
An intro clip that details what we are going to see in this section.
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Prompt Injection
Let's see how we can drive LLM's to bypass their own rules through prompt injection.
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Prompt Leaking
Check out how to force an LLM to spit out its initial instructions through prompt leaking.
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Jailbreaking
In this clip, we are exposing a technique for using prompt injections in order to make LLM's bypass their legality and ethical built-in constraints.
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The RTF Framework
Learn about this powerful prompting framework, what it is and when to use it.
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The RISEN Framework
Find out what RISEN stands for and when to use it best for optimal results.
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The RODES Framework
RODES is a very advanced prompting framework, capable of driving entire lines of business. Learn how to use it properly to drive the most effective results.
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Chain of Density
Learn about chain of density, which uses recursion to improve itself.
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Building a RAG (Retrieval-Augmented Generation) Tool with OpenAI and LangChain
Building a RAG is a great enabler for using large language models. These models are normally limited to the set of information they were trained with. With this, you empower them with private sets of information, giving them extra power and context over proprietary documents.
Here's is the RAG tool in Google Colab: https://colab.research.google.com/drive/1mVpDf4dXTiWsKkqrRl52YtGpVALZZmW0#scrollTo=rtsMFbg_BP93. You can run the code directly there, or you can create a copy for yourself in your Google Drive account.
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Building a RAG Tool with LangChain and Llama3
Check out how to build a RAG tool with LangChain and Llama 3 model.
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Create a RAG Tool With the Google Gemini Model
