A Survey of Techniques for Maximizing LLM Performance
Join us for a comprehensive survey of techniques designed to unlock the full potential of Language Model Models (LLMs). Explore strategies such as fine-tuning, RAG (Retrieval-Augmented Generation), and prompt engineering to maximize LLM performance. Speakers: John Allard Engineering Lead, Fine-tuning Product Team at @OpenAI Colin Jarvis Solutions, EMEA at @OpenAI
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Andrej Karpathy: From Vibe Coding to Agentic Engineering w/ Stephanie Zhan
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Is RAG Still Needed? Choosing the Best Approach for LLMs
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Google for Startups Immersion x Antler India: Building Agents with Gemini and ADK
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OpenAI DevDay 2024 | Balancing accuracy, latency, and cost at scale
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New Products: A Deep Dive
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Devoxx Greece 2026: Less Compute More Impact How Model Quantization Fuel the Next Wave of Agentic AI
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Evaluating LLM-based Applications
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How a reasoning model cracked an 80-year-old math problem — the OpenAI Podcast Ep. 20
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RAG vs. CAG: Solving Knowledge Gaps in AI Models
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Jeff Dean (Google): Exciting Trends in Machine Learning
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Andrej Karpathy: Software Is Changing (Again)
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Research x Product
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The Business of AI
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State of LLMs 2026: RLVR, GRPO, Inference Scaling — Sebastian Raschka
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From Zero to Your First AI Agent in 25 Minutes (No Coding)
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Building Production-Ready RAG Applications: Jerry Liu
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Visualizing transformers and attention | Talk for TNG Big Tech Day '24
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Don't learn AI Agents without Learning these Fundamentals
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How I use LLMs
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