๐ฆ๐บ๐ฎ๐ฟ๐ ๐ฆ๐ฒ๐ฎ๐ฟ๐ฐ๐ต ๐ช๐ถ๐๐ต๐ผ๐๐ ๐ ๐ผ๐ฑ๐ฒ๐น ๐ง๐ฟ๐ฎ๐ถ๐ป๐ถ๐ป๐ด
I built a documentation site for an internal tool. Users wanted a search engine with understanding. They wanted to type a question and get the right answer.
Keyword search failed. It missed answers when users used different words. I tried NLP libraries. They added too much weight to the project. I tried local models. They needed a GPU for speed.
I stopped trying to host a model. I used an AI API instead.
Here is the process:
- Break documentation into small pieces.
- Send the user query and pieces to an AI API.
- Use a prompt to find the best match.
- Return the result.
No fine-tuning needed. A good prompt and a few documents do the job.
The results:
- Search handles natural questions.
- Support tickets dropped 30%.
- No ML engineering required.
Keep these tips in mind:
- Cache common queries to save money.
- Combine AI with keyword search for speed.
- Use local models for strict privacy needs.
You do not need to be an expert to add AI. You only need the right API and a clear prompt.