LLM
An artificial intelligence model trained on vast amounts of data using billions or more parameters. It serves as the core engine of modern generative AI services, performing various intellectual tasks such as complex reasoning, summarization, and coding, in addition to natural language understanding and generation.
Detailed explanation
Why it matters for tool selection
LLMs are at the heart of enterprise workflow automation and service intelligence. Because each model differs in its Korean processing capabilities, logical reasoning speed, cost per token, and security policies, choosing the model optimized for your business objectives (customer service, data analysis, creative writing, etc.) directly impacts operational efficiency.
What to check when selecting a tool
- Does it accurately understand Korean context and cultural nuances?
- Are the API cost and response latency appropriate for the required workload?
- Does it offer an enterprise security plan (Enterprise) to prevent data leaks?
- Is the context window (amount of input information allowed at once) sufficient?
Business use cases
In the legal field, it analyzes tens of thousands of pages of case law to summarize key issues; in software development, it identifies bugs in complex logic and proposes fixes; and in marketing, it generates multilingual content in real time tailored to a brand's tone and manner.
Commonly confused terms
SLM (Small Language Model)
A model specialized for specific purposes by reducing the parameter scale. It is lighter and faster than an LLM, making it ideal for on-device AI or security-sensitive on-premise deployments.
LMM (Large Multimodal Model)
An expanded large-scale model concept that simultaneously understands and generates text, images, video, and audio.