Postgres and the Artificial Intelligence Landscape, artificial intelligence use has exploded, with much anticipation about its future. This talk explores many of the advances that has fueled this explosion, including multi-dimensional vectors, text embeddings, semantic/vector search, transformers, generative AI, and Retrieval-Augmented Generation (RAG). The talk includes semantic/vector search and RAG examples. It covers how the valuable data stored in databases can be used to enhance AI usage.

In this talk, you will learn about GraphRAG, a technique that combines graph databases with generative AI to improve the quality of LLM-generated content. We will explore the terms Retrieval-Augmented Generation (RAG) and Context Engineering, and how GraphRAG can be used in both scenarios. The topic is aimed at Generative AI practitioners who are familiar with vector-based Retrieval-Augmented Generation (RAG) and would like to understand how the approach of GraphRAG can improve the quality of LLM-generated content.

This 2-hour intensive session provides a foundational and practical guide to the burgeoning field of prompt engineering. As large language models become a core part of professional workflows, the ability to communicate with them effectively is crucial. This course will demystify the art and science of crafting powerful prompts.\n\nWe'll move beyond simple queries and introduce you to the fundamentals of structured prompting, showing you how small changes can lead to dramatically better results. You will learn to optimize your prompts for clarity, specificity, and control to ensure the AI delivers the responses you need.\n\nBy the end of this session, you'll be able to:\n- Recognize the limitations of basic prompting and avoid common trial‑and‑error pitfalls.\n- Apply key prompting techniques for enhanced control, accuracy, and repeatable results.\n- Understand the shift to programmatic prompt engineering, exploring how templates and automation optimize AI workflows.\n- Power advanced AI applications like Retrieval‑Augmented Generation (RAG) and Agentic AI.\n- Improve AI reasoning and decision‑making, learning how advanced techniques can reduce hallucinations and improve logical output.

This 2-hour intensive session provides a foundational and practical guide to the burgeoning field of prompt engineering. As large language models become a core part of professional workflows, the ability to communicate with them effectively is crucial. This course will demystify the art and science of crafting powerful prompts.

We'll move beyond simple queries and introduce you to the fundamentals of structured prompting, showing you how small changes can lead to dramatically better results. You will learn to optimize your prompts for clarity, specificity, and control to ensure the AI delivers the responses you need.

By the end of this session, you'll be able to: - Recognize the limitations of basic prompting and avoid common trial-and-error pitfalls. - Apply key prompting techniques for enhanced control, accuracy, and repeatable results. - Understand the shift to programmatic prompt engineering, exploring how templates and automation optimize AI workflows. - Power advanced AI applications like Retrieval-Augmented Generation (RAG) and Agentic AI. - Improve AI reasoning and decision-making, learning how advanced techniques can reduce hallucinations and improve logical output.

This 2-hour intensive session provides a foundational and practical guide to the burgeoning field of prompt engineering. As large language models become a core part of professional workflows, the ability to communicate with them effectively is crucial. This course will demystify the art and science of crafting powerful prompts. We'll move beyond simple queries and introduce you to the fundamentals of structured prompting, showing you how small changes can lead to dramatically better results. You will learn to optimize your prompts for clarity, specificity, and control to ensure the AI delivers the responses you need. By the end of this session, you'll be able to: - Recognize the limitations of basic prompting and avoid common trial-and-error pitfalls. - Apply key prompting techniques for enhanced control, accuracy, and repeatable results. - Understand the shift to programmatic prompt engineering, exploring how templates and automation optimize AI workflows. - Power advanced AI applications like Retrieval-Augmented Generation (RAG) and Agentic AI. - Improve AI reasoning and decision-making, learning how advanced techniques can reduce hallucinations and improve logical output.

This 2-hour intensive session provides a foundational and practical guide to the burgeoning field of prompt engineering. As large language models become a core part of professional workflows, the ability to communicate with them effectively is crucial. This course will demystify the art and science of crafting powerful prompts.

We'll move beyond simple queries and introduce you to the fundamentals of structured prompting, showing you how small changes can lead to dramatically better results. You will learn to optimize your prompts for clarity, specificity, and control to ensure the AI delivers the responses you need.

By the end of this session, you'll be able to: - Recognize the limitations of basic prompting and avoid common trial-and-error pitfalls. - Apply key prompting techniques for enhanced control, accuracy, and repeatable results. - Understand the shift to programmatic prompt engineering, exploring how templates and automation optimize AI workflows. - Power advanced AI applications like Retrieval-Augmented Generation (RAG) and Agentic AI. - Improve AI reasoning and decision-making, learning how advanced techniques can reduce hallucinations and improve logical output.

Workshop led by Alexey Grigorev on building a chatbot using large language models with Python. Topics include data extraction from FAQs, knowledge base indexing, chatbot setup in a Jupyter Notebook, interfacing with LLMs, and implementing Retrieval-Augmented Generation (RAG).

Aim of the project is to help voters find out which political parties share their views so they can make a better-informed voting decision. Ask our system about any political topic and learn what different parties think about it. Based on your question, our Retrieval-Augmented Generation (RAG) engine browses through thousands of paragraphs in party manifestos and parliament speeches to identify the parties’ perspectives. It then returns a concise summary for each party so you get a sense of what they stand for. We hope this enables you to browse and understand the vast amount of information hidden within the depths of the European political landscape.

Foundations of LLMs and Python Basics; Understanding Natural Language Processing; Transformers and Attention; LLM Development: Fine-tuning and Prompt Engineering; Retrieval-Augmented Generation (RAG); Introduction to LLM Agents; Advanced Topics for Production LLM Application.