LLMs in Business: Documentation, Support and Data
Large language models (LLMs) are useful to businesses in three main places.
1. Documentation and internal knowledge
An LLM can turn long, dense documentation into short, usable answers. Connect it to the internal knowledge base, product manuals and technical docs, and people spend less time searching for information.
2. Customer support
A chatbot built on an LLM can answer customer questions quickly and consistently. Every conversation is also data: analyze them and you see recurring problems and what needs to improve.
3. Data analysis
LLMs can pull useful information out of large amounts of text such as customer feedback, support tickets and sales notes. That helps with understanding what customers need and with planning marketing.
What you need technically
- A model: OpenAI's GPT models, or a similar service, through an API.
- Data processing: ingestion pipelines, preprocessing, and semantic indexing in a vector database such as FAISS.
- Storage: a database like PostgreSQL for conversations, metadata and results, so you can analyze them later.
- Interface: a simple front end, for example with Streamlit.
The technical setup is small, so this works for a startup as well as for a large company.