RLHF
Reinforcement Learning from Human Feedback
In one line
RLHF (Reinforcement Learning from Human Feedback) trains an LLM using human preference signals so it produces more helpful, safer responses — the recipe behind the leap in ChatGPT-style quality.
Going deeper
RLHF is an alignment technique that uses human judgments to shape model behaviour. People compare two model outputs and pick the better one; that preference data trains a reward model, which is then used in reinforcement learning to nudge the LLM toward the preferred style.
From a marketing angle, RLHF is the step that turned raw GPT-3 into ChatGPT. Same underlying model, very different feel. The 'it actually understands me' quality you sense in ChatGPT comes more from RLHF than from the base model.
Variants like RLAIF (AI feedback instead of human) and DPO (Direct Preference Optimization) are now common, but the goal is the same: align the model with the kind of answers people actually want.
Sources
Related terms
AI Alignment
AI alignment is the field — and the practical work — of making AI systems behave in line with human intent, values and safety constraints.
LLMFine-tuning
Fine-tuning takes an already pretrained LLM and trains it further on a narrower dataset to specialise it for a domain, task or voice — the most common path for adapting an LLM to your own data.
LLMPretraining
Pretraining is the initial stage where an LLM is trained on huge amounts of text to learn general language capability — the step where the model absorbs most of its 'world knowledge'.
LLMLLM
A large language model (LLM) is a neural network trained on massive text corpora to understand and generate human language — the engine behind ChatGPT, Claude, Gemini and similar products.
LLMGuardrails
Guardrails are the layer of input/output checks added around an LLM to block unsafe responses, policy violations and leakage of sensitive information.
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