Rajan Hans Rajan Hans

7 Key AI Terms Everyone Should Know

  1. Transformer Architecture

The Transformer revolutionized AI by replacing recurrence with self-attention mechanisms — allowing models to weigh contextual relationships between all input tokens simultaneously. Introduced in “Attention Is All You Need” (Vaswani et al., 2017), it forms the backbone of LLMs (Large Language Models) like GPT, PaLM, and LLaMA.

  1. Retrieval-Augmented Generation (RAG)

RAG bridges generative AI and information retrieval, ensuring LLMs can access up-to-date or domain-specific knowledge without retraining. RAG mitigates hallucination, supports enterprise knowledge integration, and underpins AI copilots and knowledge assistants. Modern variations include Graph-RAG, HyDE (Hypothetical Document Embeddings), and Adaptive Context Compression.

  1. Diffusion Models

Diffusion models represent the new frontier in generative modeling, outperforming GANs in image synthesis. They work by progressively denoising random noise through a learned stochastic process — effectively reversing diffusion. Diffusion frameworks are computationally heavy but yield high-fidelity, diverse outputs and are being generalized for 3D, audio, and video generation.

  1. Self-Supervised Learning (SSL)

Self-supervised learning uses pretext tasks to learn representations from unlabeled data — the bridge between unsupervised and supervised paradigms. SSL reduces dependency on labeled datasets, making it the foundation of pretraining strategies for LLMs, vision models, and multimodal systems.

  1. Reinforcement Learning from Human Feedback (RLHF)

RLHF aligns generative models with human preferences. This technique is what makes models like ChatGPT “feel” conversationally aligned, integrating subjective judgment into algorithmic optimization.

  1. LoRA and Parameter-Efficient Fine-Tuning (PEFT)

Low-Rank Adaptation (LoRA) is a technique for fine-tuning large models efficiently by injecting trainable low-rank matrices into existing weight layers, rather than updating all parameters. This drastically reduces compute and storage needs. PEFT approaches — such as Adapters, Prefix Tuning, and BitFit — have democratized model adaptation, enabling small teams to fine-tune billion-parameter models on commodity hardware.

  1. Agentic AI (Autonomous Agents)

Agentic AI represents the next evolution: LLMs that can plan, reason, and act autonomously within environments. Examples include AutoGPT, BabyAGI, CrewAI, and LangGraph. Agentic architectures are blurring the boundary between chatbots and true autonomous systems — a major step toward persistent, goal-oriented intelligence.

Final Reflection

AI’s frontier is shifting from pattern recognition to cognitive orchestration. Understanding these advanced concepts — Transformers, RAG, Diffusion, SSL, RLHF, LoRA, and Agentic AI — means understanding where the field is headed: toward adaptive, composable, and reasoning-capable systems. For those building in this space, this vocabulary isn’t optional — it’s fluency in the language of the future.