Your Beginner's Guide to Getting Started with Generative AI
10 Days of GenAI
Over the past couple of years, I've had the privilege of building and launching Gemini Code Assist and Gemini for Google Cloud alongside Google’s talented product and engineering teams. Teaching is a passion of mine, and I've received countless requests to break down the fundamentals of Gen AI. So, I'm thrilled to share that I just put together a videos series "10 Days of Gen AI".
Whether you're a seasoned developer or just dipping your toes into the world of AI, this series will equip you with the knowledge and tools you need to harness the power of Gen AI. Let's embark on this exciting journey together!
Day 1: What is Generative AI? An Intro!
Generative AI is a type of AI that can create new things, like text, images, and music, unlike traditional AI that just analyzes information. Generative AI learns from massive datasets and uses that knowledge to generate entirely new content. It has the potential to revolutionize the way we work and create by assisting us with tasks and sparking creativity. Watch the video for the introduction.
Day 2: Large Language Model explained
LLMs, a type of generative AI for text, are like language whizzes that have absorbed a massive library of written works. This gives them a deep understanding of language nuances, enabling them to write, summarize, translate, chat, and answer questions. Under the hood, LLMs use a complex neural network trained on tons of text data, broken down into smaller chunks called tokens. These tokens are assigned unique numerical representations called embeddings, which capture the meaning and context of words, allowing the LLM to grasp relationships between them. With millions or even billions of adjustable parameters, LLMs can learn increasingly complex language patterns. Watch the Day 2 video to learn more!
Day 3: Getting started with Generative AI
Ready to jump into generative AI? It's easier than you might think! Just follow these four steps:
First, pick your AI tool – Gemini, Claude, ChatGPT, or another favorite.
Next, craft a clear and detailed prompt – think of it as giving the AI directions. The more specific, the better!
Then, hit "generate" and let the AI do its thing.
Finally, don't be afraid to experiment – tweak your prompt or try different settings to refine the results. It's all about finding the perfect blend of creativity and control.
Day 4: A guide to the different types of Prompt Engineering
Prompt engineering is like giving AI a cheat sheet for better answers. This Day 4 video covers four common techniques: zero-shot prompting (asking directly without context), one-shot prompting (giving one example), few-shot prompting (giving several examples), and chain-of-thought prompting (guiding AI step-by-step through a problem). Each technique serves a different purpose depending on the complexity of the task and the level of guidance needed by the AI. By mastering these techniques, you'll be well on your way to unlocking the full potential of generative AI.
Day 5: What is Retrieval Augmented Generation (RAG)?
Want to make GenAI responses more accurate and based on your specifc knowledge base? RAG is the solution! Retrieval Augmented Generation empowers AI to access external sources like databases and documents in real-time. This means AI can now answer your questions with the most up-to-date and relevant information, just like a super-smart research assistant. Imagine a customer service chatbot that pulls information directly from company resources to give you accurate answers – that's RAG in action. With RAG, GenAI becomes more reliable, trustworthy, and capable, opening up a world of possibilities across various applications.
Day 6: Fine tuning LLMs
Fine-tuning takes the power of Large Language Models to the next level. It's like taking an all-around athlete and turning them into a gold medal specialist. You start with a pre-trained AI model and then train it on a carefully crafted dataset to teach it the specific skills you need. Imagine teaching an AI to diagnose skin cancer by showing it thousands of labeled images. This process fine-tunes the AI's abilities, allowing it to make more accurate and specialized decisions. While fine-tuning unlocks incredible potential, it's important to be aware that it can be computationally expensive, require a lot of data, and may lead to overfitting if not done carefully.
Day 7: Difference between Fine Tuning and RAG?
Both RAG and fine-tuning are powerful tools for improving LLM performance, but they tackle the problem from different angles. Fine-tuning specializes the model's knowledge on a specific task by retraining it with additional data, while RAG enhances its knowledge in real-time by accessing external sources. Fine-tuning excels at tasks requiring deep expertise where training data is readily available, while RAG shines in situations where information changes frequently and factual accuracy is critical. The choice between the two depends on your project's specific needs and resources. In some cases, combining RAG and fine-tuning can offer the best of both worlds, providing both up-to-date information and specialized knowledge.
Day 8: AI Embeddings Explained in 3 mins!
Embeddings are transforming the way AI understands information. Imagine a library where books are organized not just by keywords but by their deeper meaning. Embeddings are like a map where AI places books based on their themes, ideas, and writing styles, allowing it to find similar books even if they don't share the same keywords. This enables AI to recommend books based on your interests, analyze large amounts of text data for trends, and provide more accurate search results. Think of it as AI's way of truly understanding what you're looking for, not just the words you use.
Day 9: Vector Search and Vector Databases Explained in 3 mins!
Vector search and vector databases are like having a super-intelligent librarian who instantly understands your interests and can find exactly what you're looking for, even if you don't know the exact words to use. Vector search translates your query into a point on a map of embeddings, where each point represents a piece of information. It then swiftly locates the closest points, which are the most semantically similar items, regardless of keywords. Vector databases store this information in a way that optimizes similarity search, making the process lightning-fast. This technology is revolutionizing recommendation engines, shopping experiences, and question answering, leading to more accurate and personalized results.
Day 10: How to Architect GenAI Applications
Generative AI applications use several components to create intelligent responses. When a user asks a question, the LLM provides a generic answer based on its training data. To make it more relevant, RAG enhances this response using internal knowledge. This is done by converting internal documents into embeddings, which are stored in a vector database. When a user asks a question, the query is also turned into an embedding and compared to those in the database. The closest match is then used by the LLM to generate a final response that's both comprehensive and accurate. I recommend you watch this video for sure because it summarizes how YOU can start using GenAI in your use cases.
GenAI Application Architecture copyright @pvergadia
Conclusion
And that wraps up our 10-day crash course on generative AI! We've covered a lot of ground, from the basics of LLMs and prompt engineering to advanced techniques like RAG, fine-tuning, and vector databases. You've learned how to leverage these tools to create more accurate, relevant, and personalized AI-powered applications.
But this is just the beginning of your journey into the exciting world of GenAI. The possibilities are endless, and the technology is constantly evolving. Keep experimenting, keep learning, and most importantly, keep creating!
If you found this series helpful, I encourage you to subscribe to my newsletter and follow me on YouTube where dive deeper into the resources available online and continue exploring the vast potential of generative AI. Don't forget to share your learnings and creations with the community.
