Practical AI for Research: LLMs, RAG & Agentic Systems
When
This session introduces the benefits and practicalities of running Large Language Models on local machines. It will cover Ollama, including installation, downloading models (e.g., Llama, Mistral), command-line interaction, and basic API access. It will also explore LM Studio as a user-friendly GUI for discovering, downloading, and interacting with various LLMs, including setting up a local inference server. Brief consideration of hardware requirements will be discussed.
This five-session workshop provides comprehensive training in contemporary AI deployment methodologies, instructing participants in local Large Language Model execution using frameworks such as Ollama and LM Studio, while facilitating access to open-source models through platforms like AI VERDE. The curriculum encompasses advanced implementation strategies including Retrieval Augmented Generation (RAG) systems for enhanced factual accuracy and hallucination mitigation (Lewis et al., 2020), tool calling architectures for external API integration, and automated text-to-SQL code generation.
Supplemental methodologies include AI-assisted coding techniques, which leverage language models for code completion, debugging, and optimization workflows, enabling accelerated development cycles and improved code quality. Additionally, participants will explore vibe coding approaches, an emergent paradigm emphasizing intuitive, conversational programming interfaces that facilitate rapid prototyping and iterative development through natural language specifications.
The workshop culminates with comprehensive training in agentic systems architecture, where LLMs demonstrate autonomous multi-step reasoning capabilities, strategic planning algorithms, and complex task execution pipelines. These systems represent the current frontier in artificial intelligence applications, enabling sophisticated problem-solving through iterative agent-environment interactions and goal-oriented behavior optimization.