๐๐๐ถ๐น๐ฑ๐ถ๐ป๐ด ๐ ๐ฅ๐๐ ๐ฃ๐ถ๐ฝ๐ฒ๐น๐ถ๐ป๐ฒ ๐๐ป ๐ ๐ช๐ฒ๐ฒ๐ธ๐ฒ๐ป๐ฑ
I used to think building AI apps required deep research and complex ML pipelines. I thought embeddings and vector databases were separate from normal web development.
I focused on the model and assumed everything else was easy. I was wrong.
The problem is not the model. The problem is context.
When you use an LLM with just a prompt, it relies on general knowledge. It does not know your private data or specific product details. This leads to hallucinations. The model sounds confident, but it is just guessing.
Retrieval Augmented Generation (RAG) fixes this. Instead of making the model smarter, you give it better information.
Here is how a RAG pipeline works:
- Chunking: You split large documents into small pieces. Good chunking improves accuracy. Bad chunking ruins retrieval.
- Embeddings: You convert text into numbers. This allows the system to understand meaning and intent rather than just matching keywords.
- Retrieval: You store these numbers in a vector database. When a user asks a question, the system finds the most relevant pieces of information.
- Generation: You pass the relevant pieces to the LLM. You tell the model to answer using only the provided context.
This shift changes the model from a guesser to a researcher. It stops hallucinating because it has a reference.
RAG is not an AI feature. It is a system design pattern. It combines search systems with language models.
Modern AI development is not about research. It is about engineering the flow of information. If your AI outputs are bad, do not blame the model. Check your chunking, your embeddings, and your retrieval strategy.
Source: https://dev.to/akshay_sarak/building-a-rag-pipeline-in-a-weekend-1b71
Optional learning community: https://t.me/GyaanSetuAi