Philipp Schmid is a Staff Engineer at Google DeepMind, building AI Developer Experience and DevRel initiatives. He specializes in LLMs, RLHF, and making advanced AI accessible to developers worldwide.
199 articles from this blog
A technical cheatsheet for using Google's Gemini AI models with the LangChain framework, covering setup, chat models, prompt templates, and image inputs.
Explains the architecture and workflow of OpenAI's Codex CLI, a terminal-based AI tool for chat-driven software development.
An overview of the Model Context Protocol (MCP), an open standard for connecting AI applications to external tools and data sources.
A tutorial on building a ReAct AI agent from scratch using Google's Gemini 2.5 Pro/Flash and the LangGraph framework for complex reasoning and tool use.
Explains the difference between Pass@k and Pass^k metrics for evaluating AI agent reliability, highlighting why consistency matters in production.
A tutorial on implementing function calling with Google's Gemma 3 27B LLM, showing how to connect it to external tools and APIs.
A practical guide to implementing function calling with Google's Gemini 2.0 Flash model, enabling LLMs to interact with external tools and APIs.
A tutorial on using Google's Gemini 2.0 AI models to extract structured data like invoice numbers and dates from PDF documents.
A tutorial on reproducing DeepSeek R1's RL 'aha moment' using Group Relative Policy Optimization (GRPO) to train a model on the Countdown numbers game.
A technical guide on aligning open-source large language models (LLMs) in 2025 using Direct Preference Optimization (DPO) and synthetic data.
Explains the training of DeepSeek-R1, focusing on the Group Relative Policy Optimization (GRPO) reinforcement learning method.
A guide on using Anthropic's Model Context Protocol (MCP) to connect AI agents with tools and data sources using various LLMs like OpenAI or Gemini.
A tutorial on fine-tuning the ModernBERT model for classification tasks to build an efficient LLM router, covering setup, training, and evaluation.
A technical guide on optimizing and scaling the fine-tuning of open-source large language models using Hugging Face tools in 2025.
A technical guide on deploying the QwQ-32B-Preview open-source reasoning model on AWS SageMaker using Hugging Face's tools.
A technical guide on deploying Meta's Llama 3.2 Vision model on Amazon SageMaker using the Hugging Face LLM DLC.
A technical guide on fine-tuning Vision-Language Models (VLMs) using Hugging Face's TRL library for custom applications like image-to-text generation.
A technical guide on using Google's Vertex AI Gen AI Evaluation Service with Gemini to evaluate open LLM models like Llama 3.1.
A guide to evaluating Large Language Models (LLMs) using the Evaluation Harness framework and optimized serving tools like Hugging Face TGI and vLLM.
A guide to deploying open-source LLMs like Llama 3 to Amazon SageMaker using Terraform for Infrastructure as Code.
