OpenAI has put together a pretty good roadmap for building a production RAG system. h/t @benparr
1. Start with naive RAG.
2. Tune your chunks: your splitting, chunk sizes, but also small to big strategies where you retrieve more than you embed. Forget fine tuning embedding
Are you using GPT-4 and wondering why it feels a bit more robotic these days? Like it knows only one joke?
Fairly conclusive proof that @OpenAI is now caching its responses, and in a way that ignores the temperature setting.
A few thoughts on how OpenAI is implementing RAG in the new Assistants Retrieval tool before I was locked out.
1. They're splitting on newlines. You can tell because they forget to insert the newlines back between the splits when giving you the reference (red squiggles).
2. The
If you're new to Retrieval Augmented Generation, a fantastic place to start trying it out is @jerryjliu0's new RAGs @streamlit app.
3.6K github stars in 2 weeks!
Another good article about RAG vs. Finetuning and when to use RAG with finetuning.
towardsdatascience.com/rag-vs-finetun…
One of the benefits of RAG that isn't well known: it helps reduce hallucinations.
"So in applications where suppressing falsehoods and imaginative fabrications is vital,
A few takeaways from the @OpenAI Model Spec:
cdn.openai.com/spec/model-spe…
1. GPT-5 and future models will be significantly better at decision making and instruction following.
The sheer number of conditions here, some almost contradictory, is impressive.
2. Multiple levels of
How does @OpenAI's RAG do on PDFs?
First test: table extraction. As we found with secinsights.ai, handling tables often requires special logic. @AnthropicAI clearly does that, and OpenAI Assistants doesn't, so most of the numbers it outputs are wrong.
Please don’t do this. You don’t want your customer’s first impression of your product to be “my boss just got a wtf from the security/branding/marketing/partnerships team”
