What was the system prompt?
From zero to a RAG system: successes and failures
61–70 of 129 posts
Re: From zero to a RAG system: successes and failures
#62[dead]
Re: From zero to a RAG system: successes and failures
#63[dead]
Re: From zero to a RAG system: successes and failures
#64This article came just in the nick of time. I'm in fandoms that lean heavily into fanfiction, and there's a LOT out there on Ao3. Ao3 has the worst search (and yo can't even search your account's history!), so I've been wanting to create something like this as a tool for the fandom, where we can query "what was the fic about XYZ where ABC happened?" and get hopefully helpful responses. I'm very tired of not being abl…
If you didn't already see https://news.ycombinator.com/item?id=44878151 (Building a web search engine from scratch in two months with 3 billion neural embeddings), then you might enjoy it, even if it's way overkill for your use case.
Re: From zero to a RAG system: successes and failures
#65And some have been saying that RAGs are obsolete—that the context window of a modern LLM is adequate (preferable?). The example I recently read was that the contexts are large enough for the entire "The Lord of the Rings" books. That may be, but then there's an entire law library, the entirety of Wikipedia (and the example in this article of 451 GB). Surely those are at least an order of magnitude larger than Tolkien…
I have two surprises for you:
1. Don't believe the pundits of RAG. They never implemented one.
I did many times, and boy, are they hard and have so many options that decide between utterly crappy results or fantastic scores on the accuracy scale with a perfect 100% scoring on facts.
In short: RAG is how you fill the context window. But then what?
2. How does a superlarge context window solve your problem? Context windows ain't the problem, accurate matching requirements is. What do your inquiry expect to solve? Greatest context window ever, but what then? No prompt engineering is coming to save you if you don't know what you want.
RAG is in very simple terms simply a search engine. Context window was never the problem. Never. Filling the context window, finding the relevant information is one problem, but also only part of the solution.
What if your inquiry needs a combination of multiple sources to make sense? There is no 1:1 matching of information, never.
"How many cars from 1980 to 1985 and 1990 to 1997 had between 100 and 180PS without Diesel in the color blue that were approved for USA and Germany from Mercedes but only the E unit?"
Have fun, this is a simple request.
Re: From zero to a RAG system: successes and failures
#66I implemented many RAGs and feel sorry for anyone proclaiming "RAG is dead". These folks have never implemented one, maybe followed a tutorial and installed a "Hello World!" project but that's it.
I don't want to go into detail but 100% agree with the author's conclusion: data is key. Data ingestion to be precisely. Simply using docling and transforming PDFs to markdown and have a vector database doing the rest is ridiculous.
For example, for a high precision RAG with 100% accuracy in pricing as part of the information that RAG provided, I took a week to build a ETL for a 20 page PDF document to separate information between SQL and Graph Database.
And this was a small step with all the tweaking that laid ahead to ensure exceptional results.
What search algorithm or: how many? Embeddings, which quality? Semantics, how and which exactly?
Believe me, RAG is the finest of technical masterpiece there is. I have so many respect for the folks at OpenAI and Anthropic for the ingestion processes and tools they use, because they operate on a level, I will never touch with my RAG implementations.
RAG is really something you should try for yourself, if you love to solve tricky fundamental problems that in the end can provide a lot of value to you or your customers.
Simply don't believe the hype and ignore all "install and embed" solutions. They are crap, sorry to say so.
Re: From zero to a RAG system: successes and failures
#67And some have been saying that RAGs are obsolete—that the context window of a modern LLM is adequate (preferable?). The example I recently read was that the contexts are large enough for the entire "The Lord of the Rings" books. That may be, but then there's an entire law library, the entirety of Wikipedia (and the example in this article of 451 GB). Surely those are at least an order of magnitude larger than Tolkien…
I have two surprises for you: 1. Don't believe the pundits of RAG. They never implemented one. I did many times, and boy, are they hard and have so many options that decide between utterly crappy results or fantastic scores on the accuracy scale with a perfect 100% scoring on facts. In short: RAG is how you fill the context window. But then what? 2. How does a superlarge context window solve your problem? Context win…
It doesn't help that academia loooves ColBERT and will happily tell you how amazing -- and, look, for how tiny the models are, 20M params and super fast on a CPU, it is -- they are at seemingly everything if only you...
- Chunk properly;
- Elide "obviously useless files" that give mixed signals;
- Re-rank and rechunk the whole files for top scoring matches;
- Throw in a little BM25 but with better stemming;
- Carry around a list of preferred files and ideally also terms to help re-rank;
And so on. Works great when you're an academic benchmaxing your toy Master's project. Try building a scalable vector search that runs on any codebase without knowing anything at all about it and get a decent signal out of it.
Ha.
Re: From zero to a RAG system: successes and failures
#68And some have been saying that RAGs are obsolete—that the context window of a modern LLM is adequate (preferable?). The example I recently read was that the contexts are large enough for the entire "The Lord of the Rings" books. That may be, but then there's an entire law library, the entirety of Wikipedia (and the example in this article of 451 GB). Surely those are at least an order of magnitude larger than Tolkien…
> Surely those are at least an order of magnitude larger than Tolkien's prose and might still benefit from a RAG.
At some point, this is a distributed system of agents.
Once you go from 1 to 3 agents (1 router and two memory agents), it slowly ends up becoming a performance and cost decision rather than a recall problem.
Re: From zero to a RAG system: successes and failures
#69Odd to me that Elasticsearch isn't finding a second breath in these new ecosystems. It basically is that now, a RAG engine with model integration.
The old joke Zawinski made about picking regex "and now you have two problems" applies here.
If you pick Elasticsearch, useful as it is, you now have more than two problems. You have Elastic the company; Elasticsearch the tool; and also the clay-footed colossus, Java, to contend with.
Re: From zero to a RAG system: successes and failures
#70I implemented many RAGs and feel sorry for anyone proclaiming "RAG is dead". These folks have never implemented one, maybe followed a tutorial and installed a "Hello World!" project but that's it. I don't want to go into detail but 100% agree with the author's conclusion: data is key. Data ingestion to be precisely. Simply using docling and transforming PDFs to markdown and have a vector database doing the rest is ri…
Do you have any good resources for what you are describing?