Is RAG Still Needed? Choosing the Best Approach for LLMs
Large language models (LLMs) are limited by their training cutoff and lack real-time or private data knowledge, requiring context injection via retrieval augmented generation (RAG) or long context methods to integrate relevant information.
MAIN POINTS FROM TRANSCRIPT
- LLMs are static, knowing only data up to their training cutoff.
- Context injection is necessary for LLMs to access current or private data.
- RAG uses embedding models and vector databases to provide context.
- Long context bypasses databases by directly inputting data into the context window.
TAKEAWAYS
- LLMs cannot access real-time updates or private data without context injection.
- RAG relies on accurate retrieval logic for effective context provision.
- Long context methods leverage larger context windows in modern LLMs.
- Advances in context window size challenge the necessity of RAG's complexity.