Common LLM Applications
LLMs are being deployed across every industry. Understanding the most common patterns helps you identify opportunities to apply LLMs in your own work.
8 min•By Priygop Team•Updated 2026
LLM Application Patterns
- Question answering: user asks a question, LLM provides a detailed answer (ChatGPT, Gemini, Perplexity)
- Document summarization: feed a long document, get a structured summary
- Content generation: draft emails, articles, product descriptions, marketing copy
- Code assistant: explain code, suggest completions, debug errors, write tests (Copilot, Cursor)
- Data extraction: extract structured information from unstructured text
- Classification and labeling: classify customer emails by topic or sentiment at scale
- Retrieval-Augmented Generation (RAG): LLM answers questions using a knowledge base of your documents
- Customer support automation: answer FAQs, triage tickets, draft responses
Tip
Tip
The easiest way to start using LLMs in your work is via an API. OpenAI, Anthropic, and Google all offer APIs with clear documentation and free tiers for development. You can build a useful LLM-powered application with just 20-30 lines of Python code.
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Deep Learning ⊂ Machine Learning ⊂ Artificial Intelligence
Key Takeaways
- LLMs are being deployed across every industry.
- Question answering: user asks a question, LLM provides a detailed answer (ChatGPT, Gemini, Perplexity)
- Document summarization: feed a long document, get a structured summary
- Content generation: draft emails, articles, product descriptions, marketing copy