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Retrieval-Augmented Generation (RAG)
RAG grounds LLM outputs in retrieved documents via sparse, dense, or hybrid search, improving factuality, citations, and freshness…
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ReAct Prompting
ReAct prompting interleaves reasoning with tool actions and observations (Thought → Action → Observation), letting LLM agents plan,…
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Reinforcement Learning from Human Feedback (RLHF)
RLHF aligns language models by training a reward model on human preferences and optimizing the policy with RL…
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Retrieval-Augmented Generation (RAG)
RAG grounds LLM outputs in retrieved documents via sparse, dense, or hybrid search, improving factuality, citations, and freshness…
-
ReAct Prompting
ReAct prompting interleaves reasoning with tool actions and observations (Thought → Action → Observation), letting LLM agents plan,…
-
Reinforcement Learning from Human Feedback (RLHF)
RLHF aligns language models by training a reward model on human preferences and optimizing the policy with RL…