๐๐๐ถ๐น๐ฑ๐ถ๐ป๐ด ๐ฃ๐ฟ๐ผ๐ฑ๐๐ฐ๐๐ถ๐ผ๐ป ๐ฅ๐๐ ๐ฃ๐ถ๐ฝ๐ฒ๐น๐ถ๐ป๐ฒ๐
RAG blends LLM logic with factual data. It stops hallucinations.
Your pipeline determines your answer quality.
- Chunking: Find the right size. Too small loses context. Too big adds noise.
- Embeddings: Pick a model for your domain.
- Vector DBs: Use hybrid search for better results.
Prompts must be clear. Tell the LLM to use the provided context. If the answer is missing, the model should say it does not know.
Production systems need evaluation. Measure recall and faithfulness. Use real user queries to test.
Speed matters. Cache frequent queries. Use small LLMs for simple tasks.
Follow these backend rules:
- Start simple. Build for the problem you have today.
- Add logs and metrics from day one.
- Use idempotency keys to stop duplicate requests.
- Use database transactions for multiple updates.
Avoid over-engineering. Build the simplest thing. Measure it. Optimize where data shows a need.