๐ช๐ต๐ ๐ ๐ผ๐๐ ๐ง๐ฒ๐ฎ๐บ๐ ๐ข๐๐ฒ๐ฟ๐ฐ๐ผ๐บ๐ฝ๐น๐ถ๐ฐ๐ฎ๐๐ฒ ๐ฅ๐๐ (๐๐ป๐ฑ ๐๐ป๐ฑ ๐จ๐ฝ ๐๐๐ฟ๐ป๐ถ๐ป๐ด ๐ ๐ผ๐ป๐ฒ๐)
Many teams add RAG to every AI product. It sounds simple. It often becomes an expensive engineering project. You add vector databases and embedding pipelines. Now you have a new system to maintain.
LLMs have limits. They do not know your internal files. They do not know last week's policy changes. RAG solves this. It finds relevant data before the model answers.
RAG looks easy in diagrams. Real production is different.
- Your documents are messy.
- PDFs have bad formatting.
- Knowledge bases have duplicates.
You spend more time cleaning data than building AI.
Do not treat RAG as a feature. Treat it as infrastructure. It affects your security and cost. A demo takes a weekend. A real system takes months.
Some teams build for millions of documents before testing with a few thousand. This wastes money. Start small. Prove value. Scale later.
RAG is a good investment when:
- Your data changes often.
- Wrong answers create legal risks.
- You have private company data.
- You need citations for answers.
Some apps do not need it:
- Creative writing tools.
- Brainstorming assistants.
- General productivity tools.
Adding RAG here adds complexity without gain.
Focus on business outcomes first. Architecture comes second. Ask what problem retrieval solves. Do not chase complexity. Focus on value.
Source: https://dev.to/raj_07/why-most-teams-overcomplicate-rag-and-end-up-burning-money-2pcc
Optional learning community: https://t.me/GyaanSetuAi