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Don't do RAG - One counter intuitive thing I learnt past few weeks - Load whole knowledge base into prompt is actually fast, cheap & more accurate than normal RAG pipeline I was building an MCP for reading external doc, I was able to generate right code example by... show more
82,818 просмотров • 1 год назад •via X (Twitter)
Комментарии: 10
What if you want to dive into thousands of sales calls, marketing content, etc?
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Gemini has 1M context window vs Claude 200k... 50 pages is nothing, but what is the cost vs RAG for volume processing?
No offense, but RAG isn't for 50 pages, it's for 500k pages
Have you tried using Figma MCP? I’m curious if it’s possible to load prebuilt components that the AI can use during the build.
this is a good stuff. I am thinking of is cursor is able to read npm packages well or not because with not so known packages its' did a really bad job manybe we can connect something like this to a package wdyt?
This is brilliant TY 🙏 Love how context drives the performance and you can track it in helicone 👏
Do you think this is the way to go for giving “memory” to AI? I’m working on a chatbot that I want to give memory, but worried that as conversations get longer (and users have more conversations) that the cost of processing it all will be too much (currently used Claude 3.5 sonnet with caching, but still it gets expensive - like 50 cents for a single, long conversation). I’ve been wondering how I can figure out this memory issue - RAG? Create summaries with cheaper models?
We are working on big 1GB+ pdf DB. I think OCR once. Summarize to 50 pages. Send that plus relevant files to Gemini to answer in super context? Wanna join our Discord?
How about in local?
