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RAG-Powered Chatbot For Project Docs

One of the hottest topics in AI right now is RAG, or retrieval-augmented generation, which is a retrieval method used by AI tools to improve the quality and relevance of their outputs.

If you want domain-specific data using LLMs you have two options – fine-tuning the model or RAG workflow

Fin-tuning the model means, modifies the model’s weights by training it on your specific dataset. The model "learns" your content deeply, so it doesn’t need external retrieval — it knows the info internally.

Whereas, RAG combines a language model (like LLaMA or Mistral) with a search engine. When you ask a question, it retrieves relevant documents (from a knowledge base or docs) and then generates answers based on that.

🧠 Which is More Reliable Today?

RAG is more reliable and scalable in most real-world apps right now.

RAG is used when:

  • Easier to maintain
  • No need to retrain when docs change
  • Safer (less hallucination if the docs are accurate)

Fine-tuning is great when:

  • You need the model to work offline
  • You want ultra-fast responses
  • Or when your data is proprietary and doesn’t change much

Let's check out the course on how to build RAG powered chatbot called DocuMancer AI, – open-source for document assistant.