Observed Signal · Apr 10, 2026 · Explainer Article · Source: DEV Community · Impact: 2/5 · Sentiment: Neutral
Large Language Models Explained Simply
This explainer breaks down how large language models (LLMs) work, their training process, capabilities, and major security challenges. An LLM is framed as two files: a large parameter (weights) file and a small run-time code file. Training compresses roughly terabytes of internet text into gigabytes of parameters via large GPU clusters; the article gives Llama 2 70B as an example and a representative training recipe (~10 TB data, ~6,000 GPUs, ~12 days, ~$2M compute). A raw model becomes a helpful assistant through pre-training, fine-tuning (alignment), and optional RLHF. The piece covers scaling laws (more parameters/data → predictable gains), emerging tool use and multimodality, the "LLM OS" vision, and security risks like jailbreaks, adversarial attacks, prompt injection, and data poisoning.
Clear, practical explanation of LLM internals, training costs, stages of assistant development, capabilities (multimodality/tool use) and security risks — useful background for AdTech teams evaluating conversational interfaces and AI-driven features, but not a platform policy or product announcement.
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Key Takeaways & Evidence Grounding
- An LLM can be described as two files: a large parameters file (model weights) and a small run-code file (inference code).
- Meta's Llama 2 70B is cited as a ~140 GB parameter file plus a small run script that can run on consumer hardware.
- Representative training recipe: ~10 TB of internet text, ~6,000 GPUs for ~12 days, costing roughly $2 million in compute.
- Three stages to build an assistant: pre-training, fine-tuning (alignment), and optional RLHF (Reinforcement Learning from Human Feedback).
- Major security threats include jailbreak attacks, adversarial attacks, prompt injection, and data poisoning.
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