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Function Calling
Function calling lets LLMs emit structured tool invocations with validated arguments to safely call APIs and code, enabling…
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Speculative Decoding
Speculative decoding speeds up LLM inference by letting a fast draft model propose tokens that a larger model…
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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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Chain-of-Thought (CoT)
Chain-of-thought (CoT) prompts models to show intermediate reasoning steps, improving multi-step problem solving and interpretability for math, logic,…
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Prompt Injection
Prompt injection is an attack where malicious text in prompts or retrieved content hijacks an LLM or agent,…
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Instruction Tuning
Instruction tuning fine-tunes LMs on instruction–response pairs to improve adherence, helpfulness, and controllability, and often precedes preference tuning…
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Graph Retrieval-Augmented Generation (Graph RAG)
Graph RAG organizes knowledge as a graph and retrieves connected subgraphs for LLMs, enabling multi-hop reasoning, disambiguation, and…
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Function Calling
Function calling lets LLMs emit structured tool invocations with validated arguments to safely call APIs and code, enabling…
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Agentic AI
Agentic AI enables LLMs to plan, use tools, and act in closed-loop cycles with memory and safety controls,…
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Direct Preference Optimization (DPO)
DPO aligns LLMs using human preference pairs—no reward model or RL required—by training the policy to prefer chosen…
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Function Calling
Function calling lets LLMs emit structured tool invocations with validated arguments to safely call APIs and code, enabling…
-
Speculative Decoding
Speculative decoding speeds up LLM inference by letting a fast draft model propose tokens that a larger model…
-
Reinforcement Learning from Human Feedback (RLHF)
RLHF aligns language models by training a reward model on human preferences and optimizing the policy with RL…
-
Chain-of-Thought (CoT)
Chain-of-thought (CoT) prompts models to show intermediate reasoning steps, improving multi-step problem solving and interpretability for math, logic,…
-
Prompt Injection
Prompt injection is an attack where malicious text in prompts or retrieved content hijacks an LLM or agent,…
-
Instruction Tuning
Instruction tuning fine-tunes LMs on instruction–response pairs to improve adherence, helpfulness, and controllability, and often precedes preference tuning…
-
Graph Retrieval-Augmented Generation (Graph RAG)
Graph RAG organizes knowledge as a graph and retrieves connected subgraphs for LLMs, enabling multi-hop reasoning, disambiguation, and…
-
Function Calling
Function calling lets LLMs emit structured tool invocations with validated arguments to safely call APIs and code, enabling…
-
Agentic AI
Agentic AI enables LLMs to plan, use tools, and act in closed-loop cycles with memory and safety controls,…
-
Direct Preference Optimization (DPO)
DPO aligns LLMs using human preference pairs—no reward model or RL required—by training the policy to prefer chosen…