Hugo’s blog

Beyond Prompt-and-Pray

AI

LLMs

Agents

Building Reliable LLM-Powered Software in an Agentic World.

Why AI agents fail in production—and how to fix it

AI

LLMs

Agents

Newsletter

And LLM evaluation essentials.

Master LLM Application Development: Course & Free Resources

AI

LLMs

Agents

Newsletter

Free 30-min AI/ML Consultation with me - or grab the free resources below!

Building LLM Apps: Essential Resources for Data Scientists and Software Engineers

AI

LLMs

Agents

Newsletter

And a Conversation with Chief Data Scientist at The New York Times, Chris Wiggins.

Is Data Science Dead in the Age of AI?

AI

LLMs

Agents

Newsletter

🎓 Build Robust AI Systems—Scholarship Applications Now Open!

Building Reliable GenAI Systems: Lessons, Conversations, and Tools

AI

LLMs

Agents

Newsletter

Exploring the Foundations of Modern Data Science.

Escaping AI Proof-of-Concept Purgatory

AI

LLMs

Agents

Newsletter

And Building Robust, Reliable LLM-powered systems.

Michael Jordan: The Next Evolution of AI: Markets, Uncertainty, and Engineering Intelligence at Scale🔭

AI

LLMs

Agents

Newsletter

I’m building a GenAI course and would love your input 🤗

Building Reliable AI: Prompt Engineering, Fine-Tuned Models, and Efficient Workflows

AI

LLMs

Agents

Newsletter

Reliable Agentic Bots with Llama 8B: Efficiency at Scale 🤖

AI at NASA, Scaling Platforms at Uber, and the Future of Open-Source AI 🔭

AI

LLMs

Agents

Newsletter

From Prompt Engineering to AI Ethics: What’s Next for Generative AI?

How To Build A Travel AI Assistant That Doesn’t Hallucinate 🤖

AI

LLMs

Agents

Newsletter

The Future of Open-Source AI and Research.

Where are you in the GenAI Hype Cycle?

AI

LLMs

Agents

Newsletter

And Building Your First Multimodal GenAI App 🚀

Cutting AI Assistant Costs by Up to 77.8%: The Power of Enhancing LLMs with Business Logic

AI

LLMs

Agents

Newsletter

And Kicking off Data Dialogs, a new series of data, ML, and AI conversations for data leaders.

Building Reliable and Robust ML/AI Pipelines

AI

LLMs

Agents

Newsletter

From ML to AI Eng, Navigating the Shift to Foundation Models.

Rethinking Data Science, ML, and AI

AI

LLMs

Agents

Newsletter

And Systematically Improving LLM and RAG applications.

42 Lessons from a Year of Building with AI Systems

GenAI

LLMs

Findings from a three-hour livestream and two podcast episodes about lessons from building real-world applications on top of LLMs.

Hugo Bowne-Anderson

Fine-Tuning GPT-2 for Spam Classification: A Live Coding Session with Sebastian Raschka

GenAI

LLMs

Discover how to fine-tune a pre-trained GPT-2 model for spam classification in this step-by-step tutorial. Learn data preprocessing techniques, model architecture modification, and the training process using PyTorch. Understand key concepts like freezing layers, cross-entropy loss, and evaluation metrics. See how fine-tuning can significantly improve accuracy from 50% to 95%. Explore ideas for further optimization and adapting the approach to other text classification tasks like sentiment analysis and topic classification.

Hugo Bowne-Anderson and Sebastian Raschka

Developing and Training LLMs From Scratch

GenAI

LLMs

Learn the full lifecycle of building large language models (LLMs) from the ground up. Explore model architecture design, pre-training, fine-tuning, RLHF, and deployment techniques. Discover the skills and hardware needed to work with LLMs, and how to choose between prompt engineering, RAG, and fine-tuning for your use case. Stay up-to-date with cutting-edge research like LoRA, Mixture of Experts, and Direct Preference Optimization. Understand the implications of LLMs for community-driven platforms like Stack Overflow.

Hugo Bowne-Anderson and Sebastian Raschka

10 Brief Arguments for Local LLMs and AI

GenAI

LLMs

I recently hosted a session with Simon Willison for the “Mastering LLMs” conference, discussing local LLM usage with Simon’s CLI utility. Reflecting on why I prefer local LLMs, key benefits include data privacy, performance, cost efficiency, customization, offline capabilities, learning opportunities, open-source support, scalability, ethical considerations, and autonomy. Recommended tools for getting started include Ollama, Simon’s LLM CLI utility, LlamaFile, LM Studio, and Oobagooba’s text generation webUI.

Hugo Bowne-Anderson

AI and ML on the Command Line, Local LLMs, and How to Really Build Chatbots

AI

LLMs

Agents

Newsletter

Plus What we Learned from a Year of Building with LLMs and Other Events

Getting Started with Generative AI for Everyone

GenAI

LLMs

With the advent of Generative AI, more people than ever (technical and non-technical) can build interesting, fun, and productivity-increasing AI apps and workflows. In this post, we introduce the GenAI mindset of combining atomic units to build AI-powered apps and workflows

Hugo Bowne-Anderson and Jonathan Whitaker

Boost Your Productivity with ChatGPT in 2024: Simple Steps to Get Started

GenAI

LLMs

Have a 60-page PDF you don’t have time to read and want summarized? A meeting you need action items from? Or an online video you need transcribed and summarized? Would you like them to then generate a PDF, text file, or spreadsheet of the results? You can do all of this with ChatGPT.

Hugo Bowne-Anderson

ChatGPT, Author of The Quixote

GenAI

LLMs

In the era of generative AI, copyright won’t be enough. In fact, it’s the wrong place to look.

Hugo Bowne-Anderson

Lights, GenAI, Action – Building Systems with Generative Video

GenAI

Metaflow

We created a workflow to generate hundreds of videos with Stable Video Diffusion in one command.

What is Causal Inference?

Causal inference

An Introduction for Data Scientists and Machine Learning Engineers.