Large Language Models (LLMs)
Gautam AI provides a structured introduction to Large Language Models (LLMs), explaining how large-scale transformer models are trained, aligned, and deployed to power AI copilots, assistants, and enterprise intelligence systems.
What Are Large Language Models?
Large Language Models are transformer-based neural networks trained on massive text corpora to understand, generate, and reason with language.
They form the backbone of modern AI systems such as chat assistants, copilots, search engines, and decision-support platforms.
Core LLM Concepts Covered
LLM Architecture
Transformer stacks, parameters, and depth.
Pretraining
Large-scale unsupervised language modeling.
Scaling Laws
Model size, data, and compute trade-offs.
Alignment
Instruction tuning and RLHF concepts.
Inference & Deployment
Latency, cost, and system constraints.
Safety & Limitations
Hallucinations, bias, and robustness.
Who Should Learn LLMs?
- NLP practitioners moving into generative AI
- AI engineers building copilots and agents
- Researchers exploring large-scale models
- Professionals working on AI-driven products
What Comes After LLMs?
- Prompt engineering & instruction tuning
- Fine-tuning & parameter-efficient training
- Retrieval-Augmented Generation (RAG)
- β-level professional & enterprise AI systems
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