Most discourse around Large Language Models falls into two failure modes: uncritical enthusiasm or categorical dismissal. Both miss the structural reality of what these systems are and what they can do.
What LLMs Actually Are
Large Language Models are statistical pattern completers trained on massive text corpora. They are not reasoning engines. They are not databases. They are not sentient. They are sophisticated interpolation machines that generate statistically likely continuations of input sequences.
Understanding this architectural reality is essential for deploying them effectively.
What They Are Good At
LLMs excel in domains where pattern completion aligns with useful output:
- ▮Structured text generation — given a clear prompt with constraints, they produce coherent text
- ▮Format translation — converting between registers, formats, and structures
- ▮Summarization — compressing complex documents while preserving key information
- ▮Code generation — producing syntactically correct code with appropriate guardrails
- ▮Classification — categorizing text based on learned patterns
What They Cannot Do
The failure modes are equally important to understand:
- ▮Guarantee factual accuracy — they generate plausible text, not verified facts
- ▮Reason about truly novel domains — they interpolate from training data, they do not extrapolate
- ▮Replace domain expertise — they amplify existing knowledge, they do not substitute for it
- ▮Maintain consistent long-context reasoning — coherence degrades with complexity
The Correct Frame
LLMs are amplifiers. They amplify competence and they amplify incompetence. The output quality is bounded by the input quality and the operator's domain knowledge.
The organizations that derive the most value from LLMs are those that treat them as infrastructure — not as oracles. They build guardrails, validation layers, and human review into every pipeline.
The hype cycle will pass. The structural utility will remain.