◦ Generative AI creates data, while Discriminative AI categorizes data. • Applications: Content Creation, Data Augmentation, Recommendations, Customer Support, Code Generation. • Key Technologies: GPT, GAN etc.
productivity across various domains. • Copilot for creative artists. • Ability to automate tasks, improve efficiency, and reduce costs. • Can provide personalized experiences and recommendations. • Potential to solve complex problems and drive innovation.
labelled datasets. Link • Huge number of pre-trained SOTA models (eg: Llama, GPT older versions, Falcon, T5 etc.). Link • Innovative DL methods ◦ Model architectures like Transformers, GAN ◦ Training techniques like LoRA, RLHF • Advances In Computation & Hardware ◦ Nvidia Ampere (e.g. A100) & Hopper (e.g. H100, H200) series. ◦ Groq LPUs
concise, and relevant. Unoptimized prompts can lead to unintended consequences. • Use delimiters: Separate different parts of your prompt (instructions, context, examples, etc.) using clear delimiters like ###, """, —, or XML like tags to help the AI distinguish between sections. • Overall structure of the prompt: Start with role, task, instructions on how to accomplish the task, finally have styling instructions. • Provide relevant context: When possible relevant background information to help the AI understand the task better. This is typically interleaved with the task instructions. For domain-specific context/knowledge, we typically use retrieval augmented generation. • Avoid prompts that are discriminatory, offensive, or unsafe. Be mindful of the potential for bias in your prompts.
of giving an AI model input or instructions to guide it in generating a specific response. • Well-structured prompts guide the AI to generate more precise and relevant outputs. • Prompting allows users to steer AI responses in specific directions, making it easier to achieve the intended tone, format, or content. • By varying prompts, you can unlock a wide range of creative and diverse responses from the AI.
with only instructions (without examples). One/Few-shot: The model is given one/few examples to learn from before responding. Dynamic few-shot: Model is given a subset of relevant examples dynamically chosed based on user query. ReAct: The model asks clarifying questions to gather more information before responding, and interleaves reasoning and action specific generations in the process of solving the given task. Other variants: Least-to-Most prompting, Reflexion Chain-of-thought: The model thinks step-by-step, showing its reasoning before giving a final answer. Other variants like Tree-of-thought, Graph-of-thought etc. LLM Input text Output text LLM Input text Output text 2x LLM Input text Output text Nx Agent LLM Input text Output text LLM Expert 1 LLM Expert 2 Tool 1 Tool 2
Outdated information. • Domain Specific Knowledge Deficits. • To address these drawbacks: ◦ Pre-Train The LLM (If Possible) ◦ Fine Tune A Base Model ◦ Pass Information While Prompting (RAG Approach)
Getting Started With Hugging Face in 15 Minutes | Transformers, Pipeline, Tokenizer, Models • Running a Hugging Face Large Language Model (LLM) locally on my laptop | Mark Needham • https://huggingface.co/docs/transformers/en/quicktour
credits. • Add Generative AI to your Android app with the Gemini API • Gemini API: ◦ https://ai.google.dev/gemini-api/docs/quickstart?lang=android • Vertex AI: ◦ Vertex AI SDKs for #Firebase ◦ https://firebase.google.com/docs/vertex-ai/get-started?platform=android
started with the Gemini API using the Vertex AI in Firebase SDKs • Firebase Angular Building an app with Gemini in Firebase and Project IDX • AI Studio
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