Greg Loughnane

Greg Loughnane

Co-Founder & CEO at AI Makerspace

    Dr. Greg Loughnane is the Co-Founder & CEO of AI Makerspace, where he is an instructor for their AI Engineering Bootcamp. Since 2021 he has built and led industry-leading Machine Learning education programs.  Previously, he worked as an AI product manager, a university professor teaching AI, an AI consultant and startup advisor, and an ML researcher.  He loves trail running and is based in Dayton, Ohio.

    All Sessions by Greg Loughnane

    Day 1 04/23/2024
    9:30 am - 11:30 am

    Should I Use RAG or Fine-Tuning? Building with Llama 3 and Arctic Embed

    <span class="etn-schedule-location"> <span class="firstfocus">LLMs</span> </span>

    One question we get a lot as we teach students around the world to build, ship, and share production-grade LLM applications is “Should I use RAG or fine-tuning?“ The answer is yes. You should use RAG AND fine-tuning, especially if you’re aiming at human-level performance in production. In 2024 you should be thinking about using agents too! To best understand exactly how and when to use RAG and Supervised Fine-Tuning (a.k.a SFT or just fine-tuning), there are many nuances that we must consider! In this event, we’ll zoom in on prototyping LLM applications and describe how practitioners should think about leveraging the patterns of RAG, fine-tuning, and agentic reasoning. We’ll dive into RAG and how fine-tuned models and agents are typically leveraged within RAG applications. Specifically, we will break down Retrieval Augmented Generation into dense vector retrieval plus in-context learning. With this in mind, we’ll articulate the primary forms of fine-tuning you need to know, including task training, constraining the I-O schema, and language training in detail. Finally, we’ll demystify the language behind the oft-confused terms agent, agent-like, and agentic by describing the simple meta-pattern of reasoning-action and its fundamental roots in if-then thinking. Finally, we’ll provide an end-to-end domain-adapted RAG application to solve a use case. All code will be demoed live, including what is necessary to build our RAG application with LangChain v0.1 and to fine-tune an open-source embedding model from Hugging Face! You’ll learn: - RAG and fine-tuning are not alternatives, but rather two pieces to the puzzle - RAG, fine-tuning, and agents are not specific *things.* They are patterns. - How to build a RAG application using fine-tuned domain-adapted embeddings **Who should attend the event?** - Any GenAI practitioner who has asked themselves “Should I use RAG or fine-tuning?” - Aspiring AI Engineers looking to build and fine-tune complex LLM applications - AI Engineering leaders who want to understand the primary patters for GenAI prototypes Module 1: The Patterns of GenAI We will break down Retrieval Augmented Generation into dense vector retrieval plus in-context learning. With this in mind, we’ll articulate the primary forms of fine-tuning you need to know, including task training, constraining the I-O schema, and language training in detail. Finally, we’ll demystify the language behind the oft-confused terms agent, agent-like, and agentic by describing the simple meta-pattern of reasoning-action and its fundamental roots in if-then thinking. Module 2: Building a simple RAG application with LangChain v0.1 and Llama 3 Leveraging LangChain Expression Language and LangChain v0.1, we’ll build a simple RAG prototype using OpenAI’s GPT 3.5 Turbo, OpenAI’s text-3-embedding-small, and a FAISS vector store! Module 3: Fine-Tuning an Open-Source Embedding Model Leveraging Quantization via the bitsandbytes library, Low Rank Adaptation (LoRA) via the Hugging Face PEFT library, and the Massive Text Embedding Benchmark leaderboard, we’ll adapt the embedding space of our off-the-shelf model (Arctic Embed) to a particular domain! Module 4: Constructing a Domain-Adapted RAG System In the final module, we’ll assemble our domain-adapted RAG system, and discuss where we might leverage agentic reasoning if we kept building the system in the future!

    Day 1 04/23/2024
    9:30 am - 11:30 am

    Should I Use RAG or Fine-Tuning? Building with Llama 3 and Arctic Embed

    <span class="etn-schedule-location"> <span class="firstfocus">LLMs</span> </span>

    One question we get a lot as we teach students around the world to build, ship, and share production-grade LLM applications is “Should I use RAG or fine-tuning?“ The answer is yes. You should use RAG AND fine-tuning, especially if you’re aiming at human-level performance in production. In 2024 you should be thinking about using agents too! To best understand exactly how and when to use RAG and Supervised Fine-Tuning (a.k.a SFT or just fine-tuning), there are many nuances that we must consider! In this event, we’ll zoom in on prototyping LLM applications and describe how practitioners should think about leveraging the patterns of RAG, fine-tuning, and agentic reasoning. We’ll dive into RAG and how fine-tuned models and agents are typically leveraged within RAG applications. Specifically, we will break down Retrieval Augmented Generation into dense vector retrieval plus in-context learning. With this in mind, we’ll articulate the primary forms of fine-tuning you need to know, including task training, constraining the I-O schema, and language training in detail. Finally, we’ll demystify the language behind the oft-confused terms agent, agent-like, and agentic by describing the simple meta-pattern of reasoning-action and its fundamental roots in if-then thinking. Finally, we’ll provide an end-to-end domain-adapted RAG application to solve a use case. All code will be demoed live, including what is necessary to build our RAG application with LangChain v0.1 and to fine-tune an open-source embedding model from Hugging Face! You’ll learn: - RAG and fine-tuning are not alternatives, but rather two pieces to the puzzle - RAG, fine-tuning, and agents are not specific *things.* They are patterns. - How to build a RAG application using fine-tuned domain-adapted embeddings **Who should attend the event?** - Any GenAI practitioner who has asked themselves “Should I use RAG or fine-tuning?” - Aspiring AI Engineers looking to build and fine-tune complex LLM applications - AI Engineering leaders who want to understand the primary patters for GenAI prototypes Module 1: The Patterns of GenAI We will break down Retrieval Augmented Generation into dense vector retrieval plus in-context learning. With this in mind, we’ll articulate the primary forms of fine-tuning you need to know, including task training, constraining the I-O schema, and language training in detail. Finally, we’ll demystify the language behind the oft-confused terms agent, agent-like, and agentic by describing the simple meta-pattern of reasoning-action and its fundamental roots in if-then thinking. Module 2: Building a simple RAG application with LangChain v0.1 and Llama 3 Leveraging LangChain Expression Language and LangChain v0.1, we’ll build a simple RAG prototype using OpenAI’s GPT 3.5 Turbo, OpenAI’s text-3-embedding-small, and a FAISS vector store! Module 3: Fine-Tuning an Open-Source Embedding Model Leveraging Quantization via the bitsandbytes library, Low Rank Adaptation (LoRA) via the Hugging Face PEFT library, and the Massive Text Embedding Benchmark leaderboard, we’ll adapt the embedding space of our off-the-shelf model (Arctic Embed) to a particular domain! Module 4: Constructing a Domain-Adapted RAG System In the final module, we’ll assemble our domain-adapted RAG system, and discuss where we might leverage agentic reasoning if we kept building the system in the future!

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