Ankit Patel
How AI Models Get Smarter
#1about 2 minutes
How AI models are surpassing human experts
AI models are now exceeding human expert performance on comprehensive benchmarks like MMLU, which measures intelligence across various subjects.
#2about 5 minutes
The shift from labeled to unlabeled data training
The transformer architecture enabled a major shift from training on limited, human-labeled data to pre-training on vast amounts of unlabeled internet text using next-token prediction.
#3about 8 minutes
Refining models with post-training techniques
Pre-trained models are made useful for specific tasks like chatbots through post-training methods such as supervised fine-tuning and reinforcement learning from human feedback (RLHF).
#4about 3 minutes
Improving answer quality with reasoning models
Reasoning models improve accuracy by using test-time scaling, a process where the model prompts itself to double-check facts and logic before providing a final answer.
#5about 5 minutes
A practical workflow for AI application developers
Developers can build AI applications by starting with an API, using structured prompt engineering, and evaluating models in context rather than relying solely on benchmarks.
#6about 3 minutes
Implementing guardrails to secure your application
Protect your AI application from manipulation and misuse by implementing guardrails, detailed system prompts, and specialized guard models to enforce desired behaviors.
#7about 3 minutes
Building modular agentic applications with tools
Agentic applications use a modular architecture where each agent can use specific tools, often defined with natural language prompts, to perform complex tasks.
#8about 4 minutes
Q&A on model behavior and synthetic data
This Q&A covers why LLM responses are non-deterministic, how synthetic data is used for model distillation, and strategies for preventing hallucinations.
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