Preeti
Generative AI models are rapidly improving, with Google Gemini 1.5 Pro setting a record for the longest context window at 1 million tokens. This has sparked a debate on whether retrieval augmented generation (RAG) is still necessary. RAG combines the power of large language models (LLMs) with external knowledge sources to produce more informed responses. However, the increasing context window of LLMs may make RAG less essential as LLMs become more adept at handling extensive context.
RAG optimizes factors like time, efficiency, and cost by selectively retrieving relevant information. Fine-tuning LLMs as an alternative to RAG can be expensive and challenging due to data limitations, computational resources, and expertise required. Integrating LLMs with big data using advanced SQL vector databases like MyScaleDB can enhance their effectiveness and improve intelligence extraction.
While LLM technology is evolving rapidly, the balance between query quality and cost remains crucial for large enterprises using generative AI. RAG and vector databases play a key role in achieving this balance. Developers are encouraged to explore tools like MyScaleDB on GitHub and engage in discussions about LLMs and RAG. Subscribe to The New Stack’s YouTube channel for more insights and updates on the latest tech trends.
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Source link: https://thenewstack.io/do-enormous-llm-context-windows-spell-the-end-of-rag/
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