From Generic to Specific: Making AI Work for Your Domain

Generative AI is trained on vast amounts of data and is widely used for:
✅ Generating new ideas
✅ Writing & debugging code
✅ Automating repeated tasks
✅ Image generation
…and much more.

But what if we want AI to answer questions on a specific topic?

• Education → AI agents fine-tuned on a university’s curriculum to help students with course-specific Q&A.
• Travel → AI trained on local attractions, food options, and transport to suggest personalized itineraries.

This is where two powerful techniques come in:

🔹 Fine-Tuning – retraining a Large Language Model (LLM) so it learns domain-specific knowledge and responds only in that context.

🔹 Retrieval Augmented Generation (RAG) – connecting an LLM to external datasets/documents so it can “retrieve” relevant, up-to-date information before generating responses, leading to more accurate, context-specific and reliable outputs.

Together, these approaches make AI more focused, reliable, and domain-specific—transforming it from a general assistant into a specialized expert.