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Building LLM applications for production
Building LLM applications for production
This post consists of three parts. Part 1 discusses the key challenges of productionizing LLM applications and the solutions that I’ve seen. Part 2 discusses how to compose multiple tasks with control flows (e.g. if statement, for loop) and incorporate tools (e.g. SQL executor, bash, web browsers, third-party APIs) for more complex and powerful applications. Part 3 covers some of the promising use cases that I’ve seen companies building on top of LLMs and how to construct them from smaller tasks.
This post consists of three parts. Part 1 discusses the key challenges of productionizing LLM applications and the solutions that I’ve seen. Part 2 discusses how to compose multiple tasks with control flows (e.g. if statement, for loop) and incorporate tools (e.g. SQL executor, bash, web browsers, third-party APIs) for more complex and powerful applications. Part 3 covers some of the promising use cases that I’ve seen companies building on top of LLMs and how to construct them from smaller tasks.
·huyenchip.com·
Building LLM applications for production
State of GPT | BRK216HFS
State of GPT | BRK216HFS
Learn about the training pipeline of GPT assistants like ChatGPT, from tokenization to pretraining, supervised finetuning, and Reinforcement Learning from Hu...
·youtube.com·
State of GPT | BRK216HFS
The Dire Defect of ‘Multilingual’ AI Content Moderation
The Dire Defect of ‘Multilingual’ AI Content Moderation
Social media companies claim new language models can remove harmful content in every language. But those systems’ shortcomings can have vast consequences.
·wired.com·
The Dire Defect of ‘Multilingual’ AI Content Moderation