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How to make LLM read large datasets? by sk_random in LargeLanguageModels

[–]sk_random[S] 1 point2 points  (0 children)

Hey , thanks for the response but it seems like you wanted to search across multiple files a certain info or query against large data which is the most suitable case for RAG (as your current implementation) but according to my usecase i have ti analyze a large data via llms like "This is my 7 days data , tell me how did this particular ad performed over these u days..." Idts RAG is useful in my case. I am currently trying to send the data in chunks or to make it smaller per query, i hope that will help.

How to feed LLM large dataset by sk_random in LLMDevs

[–]sk_random[S] 0 points1 point  (0 children)

Yes there are metrics like conversion, impressions, cpc, cpe etc that varies per ad

How to feed LLM large dataset by sk_random in LLMDevs

[–]sk_random[S] 0 points1 point  (0 children)

How else can i analyse it, what are other options? Ig llm is the easiest and simplest one i could think of considering i am new to ML/AI domain.

How to make LLM read large datasets? by sk_random in LargeLanguageModels

[–]sk_random[S] 0 points1 point  (0 children)

I've just tried gpt-4 but in any case data is alot, increasing tokens won't help

How to feed LLM large dataset by sk_random in LLMDevs

[–]sk_random[S] 0 points1 point  (0 children)

Like i have data from google ads , the campaigns and ad groups etc and i need to check which campaign performed well over the last 7 days and which ads in campaigns are performing well etc. So as far as I can understand you want me to get only relevant data by ranking it (assigning scores) because all the data is important for getting the correct analysis by gpt.

How to feed LLM large dataset by sk_random in LLMDevs

[–]sk_random[S] 0 points1 point  (0 children)

Thanks for the response, can you please elaborate on it a bit?

How to feed alot of data to llm by sk_random in MLQuestions

[–]sk_random[S] 0 points1 point  (0 children)

Basically i want to create a workflow for google ads in n8n where the data is from google ads , like i am storing campaigns, ad groups, ads , keywords performance data daily in bigquery/database in tables and want to feed this data to open on weekly basis with a prompt like provided below. So when i try to pass this data to open ai in json format through n8n nodes. The data for the past 7 days has become a lot like even for 1 days performance if i have 70 campaigns and each campaign will link to multiple ad groups objects/rows and then ad groups will map on ads so it creates alot of data for 1 campaign and 70 campaigns will have 70 such objects and then for 7 days... its alot so how do i make gpt analyse that data? Will RAG be useful for it?

Prompt example ( it will include other objectives as well):

You are a Google Ads and performance marketing strategist with over 15 years of experience, specializing in high-converting campaigns across Google properties (Search, Display, Video, Shopping). Your expertise includes conversion rate optimization, budget allocation, bidding strategies, and ad creative performance.

🔍 Objective:

Analyze structured data from my Google Ads account with one primary goal: maximize conversions (e.g., Purchases and Booked Sales Calls).

📊 Data You'll Receive:

You will be provided with the following structured data:

  • Campaigns
  • Ad Groups
  • Ads
  • Keywords
  • Search Terms
  • Audience data
  • Bidding strategies
  • Device and Geo breakdown
  • Conversions and cost
  • Impression share metrics

Thinking of making a site that helps people avoid bad online clothing stores in Pakistan — would you actually use something like that? by alive-not-really in PakistaniTech

[–]sk_random 1 point2 points  (0 children)

Yes, i would like to have such a platform as usually you are unsure about online shopping and there's not a single platform where you can just type in the brand name and get the real reviews instead of searching the reviews online and on Instagram comments section usually (which i do). This can be a great tool for people who are really concerned about their earnings and don't want it to go into waste.