Greg Kamradt on X: "Pressure Testing GPT-4-128K With Long Context Recall 128K tokens of context is awesome - but what's performance like? I wanted to find out so I did a “needle in a haystack” analysis Some expected (and unexpected) results Here's what I found: Findings: * GPT-4’s recall… https://t.co/nHMokmfhW5" / X - discu.eu

Linking pages

RAG vs. Context-Window in GPT-4: accuracy, cost, & latency https://ai88.substack.com/p/rag-vs-context-window-in-gpt4-accuracy-cost 9 comments
RAG is Dead. Long Live RAG! - Qdrant https://qdrant.tech/articles/rag-is-dead/ 1 comment
Can We Prevent LLMs From Hallucinating? - Brett DiDonato https://brettdidonato.substack.com/p/can-we-prevent-llms-from-hallucinating 1 comment
GitHub - gkamradt/LLMTest_NeedleInAHaystack: Doing simple retrieval from LLM models at various context lengths to measure accuracy https://github.com/gkamradt/LLMTest_NeedleInAHaystack 0 comments
Multi Needle in a Haystack https://blog.langchain.dev/multi-needle-in-a-haystack/ 0 comments

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