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🧵 Exploring how LLMs handle reasoning and the potential for architectural tweaks to enhance their capabilities. This isn't just about getting the right answers; it's about how the model processes and evolves its responses.
Building Multi-Agent RAG with LlamaIndex + @crewAIInc 💫 CrewAI is one of the most popular and intuitive frameworks for building multi-agent systems - define a “crew” of agents with different roles that work together to solve a task. You can now easily augment these agents with… https://t.co/JlUuULtQeE
Optimizing your LLM apps? See how we refactored Wandbot, our LLM-powered doc assistant, for better efficiency and speed. Discover how we used evaluation-driven development to boost correctness from 72% to 81% and cut latency by 84%. 👉 https://t.co/o2ZkAXsHKi https://t.co/sHDKCUaD8T
Recent advancements in AI technology have led to the introduction of a new framework called Mixture of Agents (MoA) by Together AI, leveraging the strengths of multiple Large Language Models (LLMs) to enhance quality. Companies like FactoryAI are utilizing LLMs to double their iteration speed and improve performance. Additionally, optimizing LLM applications has shown significant efficiency gains, with Wandbot achieving a correctness boost from 72% to 81% and an 84% reduction in latency.