🕳️ Attention Sinks in LLMs for endless fluency
Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs
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In 2017, Google published “Attention Is All You Need,” one of the most important papers in the history of machine learning. Building on the work of Bahdanau and his colleagues, Google researchers dispensed with the RNN and its hidden states. Instead, Google’s model used an attention mechanism to scan previous words for relevant context.
Timothy B. Lee • Why large language models struggle with long contexts
The fact that adding keywords like Let’s Think Step By Step , adding “Greg Rutkowski”, prompt weights, and even negative prompting are still so enormously effective, is a sign that we are nowhere close to perfecting the “language” part of “large language models”.
Swyx • Why "Prompt Engineering" and "Generative AI" are overhyped
LLMs absorb superhuman quantities of information at training time.
Timothy B. Lee • Why large language models struggle with long contexts
Langfuse is an open source observability & analytics solution for LLM-based applications. It is mostly geared towards production usage but some users also use it for local development of their LLM applications.
Langfuse is focused on applications built on top of LLMs. Many new abstractions and common best practices evolved recently, e.g. agents,... See more
Langfuse is focused on applications built on top of LLMs. Many new abstractions and common best practices evolved recently, e.g. agents,... See more
langfuse • GitHub - langfuse/langfuse: Open source observability and analytics for LLM applications
8. Provide refreshers on forgotten or infrequently used tools.