Speaker
Abstract
The rise of LLMs that coherently use language has led to an appetite to ground the generation of these models in facts and private collections of data. This is motivated by the desire to reduce the hallucinations of these models, as well as supply them with up-to-date, often private information that is not a part of their training data. Retrieval-augmented generation is the method that uses a search step to ground models in relevant data sources. In this talk, we'll cover the common schematics of RAG systems and tips on how to improve them.
Interview
I explore and advise enterprises and the developer community on applications of Large Language Models (LLMs).
I aim to give builders the intuition of problem-solving with LLMs and going beyond thinking of them as text-in / text-out monoliths.
This talk is accessible to a wide audience. All that's needed is curiosity around large language models.
Insight into different possible systems to build using LLMs as individual components in a pipeline.
How beautiful a melodica sound with algorithmically generated music in the background is. This is from the session Functional Composition by Chris Ford.
Topics
QCon London 2024 is a three day conference for senior software engineers, architects and team leads. An international program committee of working engineers selects every session. Patterns and practices, not products and pitches.
From the same track
Monday 8 April
10:35 Windsor (5th Fl.) Session AI/ML Retrieval-Augmented Generation (RAG) Patterns and Best Practices Jay Alammar Director & Engineering Fellow @Cohere & Co-Author of "Hands-On Large Language Models" The rise of LLMs that coherently use language has led to an appetite to ground the generation of these models in facts and private collections of data. 11:45 Mountbatten (6th Fl.) Session AI/ML Navigating LLM Deployment: Tips, Tricks, and Techniques Meryem Arik Co-Founder and CEO @Doubleword (Previously TitanML), Recognized as a Technology Leader in Forbes 30 Under 30, Recovering Physicist Self-hosted Language Models are going to power the next generation of applications in critical industries like financial services, healthcare, and defence. 13:35 Mountbatten (6th Fl.) Session AI/ML Reach Next-Level Autonomy with LLM-Based AI Agents Tingyi Li Enterprise Solutions Architect @AWS Generative AI has emerged rapidly since the release of ChatGPT, yet the industry is still at its very early stage with unclear prospects and potential. 14:45 Mountbatten (6th Fl.) Session AI/ML LLM and Generative AI for Sensitive Data - Navigating Security, Responsibility, and Pitfalls in Highly Regulated Industries Stefania Chaplin, Azhir Mahmood As large language models (LLM) become more prevalent in highly regulated industries, dealing with sensitive data and ensuring the security and ethical design of machine learning (ML) models is paramount. 15:55 Whittle (3rd Fl.) Session AI/ML The AI Revolution Will Not Be Monopolized: How Open-Source Beats Economies of Scale, Even for LLMs Ines Montani Co-Founder & CEO @Explosion, Core Developer of spaCy With the latest advancements in Natural Language Processing and Large Language Models (LLMs), and big companies like OpenAI dominating the space, many people wonder: Are we heading further into a black box era with larger and larger models, obscured behind APIs controlled by big… 17:05 Fleming (3rd Fl.) Session AI/ML How Green is Green: LLMs to Understand Climate Disclosure at Scale Leo Browning First ML Engineer @ClimateAligned Assessment of the validity of climate finance claims requires a system that can handle significant variation in language, format, and structure present in climate and financial reporting documentation, and knowledge of the domain-specific language of climate science and finance.