Cedric Clyburn

Building AI apps is one thing—but making them chat with your documents is next-level. In Part 3 of the Llama Stack Tutorial, we dive into Retrieval Augmented Generation (RAG), a pattern that lets your LLM reference external knowledge it wasn't trained on. Using the open-source Llama Stack project from Meta, you'll learn how to:- Spin up a local Llama Stack server with Podman- Create and ingest documents into a vector database- Build a RAG agent that selectively retrieves context from your data- Chat with real docs like PDFs, invoices, or project files, using Agentic RAGBy the end, you'll see how RAG brings your unique data into AI workflows and how Llama Stack makes it easy to scale from local dev to production on Kubernetes.

Building AI applications is more than just running a model — you need a consistent way to connect inference, agents, storage, and safety features across different environments. That’s where Llama Stack comes in. In this second episode of The Llama Stack Tutorial Series, Cedric (Developer Advocate @ Red Hat) walks through how to:- Run Llama 3.2 (3B) locally and connect it to Llama Stack- Use the Llama Stack server as the backbone for your AI applications- Call REST APIs for inference, agents, vector databases, guardrails, and telemetry- Test out a Python app that talks to Llama Stack for inferenceBy the end of the series, you’ll see how Llama Stack gives developers a modular API layer that makes it easy to start building enterprise-ready generative AI applications—from local testing all the way to production. In the next episode, we'll use Llama Stack to chat with your own data (PDFs, websites, and images) with local models.🔗 Explore MoreLlama Stack GitHub: https://github.com/meta-llama/llama-stackDocs: https://llama-stack.readthedocs.io5.

AI applications are moving fast—but building them at scale is hard. Local prototypes often don’t translate to production, and every environment seems to require a different setup. Llama Stack, an open-source framework from Meta, was created to bring consistency and modularity to generative AI applications. In this first episode of The Llama Stack Tutorial Series, Cedric (Developer Advocate @ Red Hat) explains what Llama Stack is, why it’s being compared to Kubernetes for the AI world, key building blocks, and future episodes that'll dive into real-world use cases with Llama Stack. Explore MoreLlama Stack Tutorial (what we'll be following during the series): https://rh-aiservices-bu.github.io/llama-stack-tutorial Llama Stack GitHub: https://github.com/meta-llama/llama-stackDocs: https://llama-stack.readthedocs.io5.

llm-d optimizes LLM inference at scale with disaggregated prefill/decode, smart caching, and Kubernetes-native architecture for production environments.

Learn about the Podman AI Lab and how you can start using it today for testing and building AI-enabled applications. As an extension for Podman Desktop, the container & cloud-native tool for application developers and administrators, the AI Lab is your one-stop-shop for popular generative AI use cases like summarizers, chatbots, and RAG applications. In addition, from the model catalog, you can easily download and start AI models as local services on your machine. We'll cover this and more, and be sure to try out the Podman AI Lab today!

Wanting to use your personal or organizational data in AI workflows, but it's stuck in PDFs and other document formats? Docling is here to help. It’s an open-source tool from IBM Research that converts files like PDFs and DocX into easy-to-use Markdown and JSON while keeping everything structured. In this video, join developer advocate Cedric Clyburn to see how it works, We'll walk through a demo using LlamaIndex for a question-answering app, and share some interesting details and benchmarks. Let’s dig in and see how Docling can make working with your data so much easier for RAG, Fine-Tuning models, and more.

The rise of large language models (LLMs) has opened up exciting possibilities for developers looking to build intelligent applications. However, the process of adapting these models to specific use cases can be difficult, requiring deep expertise and substantial resources. In this talk, we'll introduce you to InstructLab, an open-source project that aims to make LLM tuning accessible to developers and data scientists of all skill levels, on consumer-grade hardware.We'll explore how InstructLab's innovative approach combines collaborative knowledge curation, efficient data generation, and instruction training to enable developers to refine foundation models for specific use cases. Through a live demonstration, you’ll learn how IBM Research has partnered with Red Hat to simplify the process of enhancing LLMs with new knowledge and skills for targeted applications. Join us to explore how InstructLab is making LLM tuning more accessible, empowering developers to harness the power of AI in their projects.

Let's take a look at how to effectively integrate Generative AI into an existing application through the InstructLab project, an open-source methodology and community to make LLM tuning accessible to all! Learn about the project, and how InstructLab can help to train a model on domain-specific skills and knowledge, then how Podman's AI Lab allows developers to easily setup an environment for model serving and AI-enabled application development.