Model Fine-tuning - Fine-tuning enables the LLM to generate reasoning tokens in French before the final English response [1] - The video demonstrates the LLM's behavior before and after fine-tuning [1]

AI Engineering Landscape - The AI engineering community is broad, technical, and growing, with the "AI Engineer" title expected to gain more ground [5] - Many seasoned software developers are AI newcomers, with nearly half of those with 10+ years of experience having worked with AI for three years or less [7] LLM Usage and Customization - Over half of respondents are using LLMs for both internal and external use cases, with OpenAI models dominating external, customer-facing applications [8] - LLM users are leveraging them across multiple use cases, with 94% using them for at least two and 82% for at least three [9] - Retrieval-Augmented Generation (RAG) is the most popular customization method, with 70% of respondents using it [10] - Parameter-efficient fine-tuning methods like LoRA/Q-LoRA are strongly preferred, mentioned by 40% of fine-tuners [12] Model and Prompt Management - Over 50% of respondents are updating their models at least monthly, with 17% doing so weekly [14] - 70% of respondents are updating prompts at least monthly, and 10% are doing so daily [14] - A significant 31% of respondents lack any system for managing their prompts [15] Multimodal AI and Agents - Image, video, and audio usage lag text usage significantly, indicating a "multimodal production gap" [16][17] - Audio has the highest intent to adopt among those not currently using it, with 37% planning to eventually adopt audio [18] - While 80% of respondents say LLMs are working well, less than 20% say the same about agents [20] Monitoring and Evaluation - Most respondents use multiple methods to monitor their AI systems, with 60% using standard observability and over 50% relying on offline evaluation [22] - Human review remains the most popular method for evaluating model and system accuracy and quality [23] - 65% of respondents are using a dedicated vector database [24] Industry Outlook - The mean guess for the percentage of the US Gen Z population that will have AI girlfriends/boyfriends is 26% [27] - Evaluation is the number one most painful thing about AI engineering today [28]

[Music] Thanks Alex for the introduction. That was a very impressive LLM generated summary of me. Uh I've never heard it before but uh nice.Um so um today I'm going to talk to you about something that has been keeping me up at night. Uh probably some of you too. So how to build enterprise aware agents.How to bring the brilliance of AI into the messy complex realities of uh how your business operated. So let's jump straight to the hottest question of the month for AI builders. Uh should I build workflows or ...

LLM Training Stages - LLM 从零开始训练包含四个阶段 [1] - 第一步是使用随机初始化的模型 [2] - 之后在大规模语料库上进行预训练 [2] - 使用指令微调使其能够遵循命令 [2] - 使用偏好和推理微调来优化响应 [2]

Machine Learning Paradigms - Four machine learning training paradigms are visually explained [1] - The paradigms include Transfer Learning, Fine-tuning, Multi-task Learning, and Federated Learning [1]

4 machine learning training paradigms, explained visually:- Transfer Learning- Fine-tuning- Multi-task Learning- Federated Learning https://t.co/q6wnDTQtIn ...

Model Fine-tuning Overview - The document outlines the process of fine-tuning models like DeepSeek-R1 [1] - The process includes dataset preparation, LoRA configuration, trainer definition, fine-tuning, and exporting to Ollama [1] Technical Implementation - The fine-tuning process of DeepSeek-R1 (distilled Llama) can be done 100% locally [1]

AI Model Fine-Tuning and Prompt Engineering - Workshop covers SFT, DPO, RFT, prompt engineering/optimization, and agent scaffolding [1] OpenAI Expertise - Ilan Bigio, a founding member of OpenAI's Developer Experience team, leads technical development for Swarm, the precursor to the Agents SDK [1] - Ilan Bigio contributed to Codex CLI and created the AI phone ordering demo showcased at DevDay 2024 [1] - Ilan Bigio partnered with companies like Cursor, Khan Academy, and Klarna to shape their AI products [1] AI Application and Development - Ilan Bigio created ShellAI, an open-source, AI-powered terminal assistant [1] - OpenAI provides in-depth technical guides on topics like Function Calling, Latency Optimization, and Agent Orchestration [1] Educational Background - Ilan Bigio designed and taught courses at Brown [1]