Buzzwords for the Busy: LLMs (www.dvsj.in)

Think of a large language model (LLM) like a toddler who repeatedly guesses the next word — it doesn’t “understand” meaning, it predicts the most likely next token given everything it has seen. Generation is just repeated next-token prediction (autoregressive inference) until a stop token ends the sequence. Training is self-supervised: mask or remove tokens, have the model predict them, compare to ground truth and update weights. That explains common failure modes: poor math or up-to-date facts (models learn statistical patterns, not symbolic calculation or real-time facts), and hallucinations (plausible—but not verified—next words). AGI remains a distinct, unresolved concept; creative outputs can seem novel but are still recombinations of learned patterns. Practical implications center on prompting, context and safety. Rich prompts narrow the token distribution so outputs are more relevant; roleplay/script framing and the system prompt steer behavior in practice. Because LLMs have no persistent memory, the full conversation is typically sent each turn, constrained by a finite context window measured in tokens — which affects cost and design (tokens are billable; the example cited GPT‑5 at ~$1.25/1M input and $10/1M output tokens). Safeguards come from curated training data, system prompts, and runtime guardrails; adversarial “jailbreaks” try to bypass those but defenses have evolved. To improve domain performance, teams choose between fine-tuning (expensive, broad) and retrieval-augmentation or context engineering (cheaper, targeted). Understanding these mechanics helps practitioners craft prompts, manage costs, and design safer, more accurate AI systems.