Activity
Mon
Wed
Fri
Sun
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
What is this?
Less
More

Memberships

Data Alchemy

38k members • Free

7 contributions to Data Alchemy
How to build reliable systems with LLMs
Are you using the OpenAI API in your projects? If so, then I'm going to tell you right now that there is probably a better way to do that. In this video, I share everything that you need to know about getting structured output from OpenAI in order to build more reliable systems with LLMs. We compare prompting techniques, JSON mode, function calling, and the Instructor library to find the best way to integrate OpenAI models into applications. The video is linked below. I hope you find it helpful. If you do, consider subscribing :)
3 likes • May '24
@Dave Ebbelaar , thanks for another excellent tutorial. Extremely helpful content! 🤗 I have a question that might be relevant for a variety of LLM use cases and could potentially be covered in another tutorial: In real-world LLM use cases, classification tasks often involve more than just 3-5 categories, such as in the sentiment analysis example you showed. Instead, these tasks frequently require selecting the right category from hundreds or even thousands. Additionally, potential categories are often stored in separate data tables, which means they need to be synchronized with your code. I was wondering if you have found a way to handle these kinds of cases using Instructor too? This could also be a great follow-up showcase for your brilliant series of YouTube tutorials!
RAG Evaluation
Hi everyone, I'm sure many of you are deeply involved in experimenting with or even implementing various RAG (Retrieval-Augmented Generation) systems. You've likely noticed how challenging it is to compare different systems or to make definitive statements about their effectiveness. I'm curious to know what evaluation methods you've tried and which ones you prefer. Personally, I've developed a small framework based on the approach suggested by Databricks (https://www.databricks.com/blog/LLM-auto-eval-best-practices-RAG). This involves using a combined score to measure correctness, completeness, and readability. I also attempted to use the RAGAS (https://github.com/explodinggradients/ragas) project, but I found it difficult to integrate with APIs other than the official OpenAI GPT ones. I'm looking forward to hearing your recommendations!
I want your suggestion...
Hii Comunity! I want to use Vector Database in one of my AI Application and I want you all to suggest a good database. I know that there is no best Database for all kinds of applications. But I want to know in general which database is good for most tasks according to your experience. Select one of the below option and also write the reason why you have selected that option.
Poll
1 member has voted
2 likes • Dec '23
my favorites: non commercial: faiss + croma commercial: qdrant + acs
Github Copilot and Newer Technologies - Open Spource Coding LLM?
The biggest problem with Copilot is that is unaware of emerging technologies and newer frameworks. About 5 months ago a create a RAG app loaded up with the github websites for technologies I use, and it seemed to do ok. I may need to try again. Is anyone aware of a good opensource coding LLM that performs reasonably well?
7 likes • Dec '23
My favorite is codeium. It has same issues wrt emerging technologies you described but it's completely free for private usage: https://codeium.com/ I would also be interested in a chatbot with better knowledge around RAG, LLM, Langchain, etc. development!
0 likes • Dec '23
@Bastian Brand well, tough these days to make a living solely from music...
1-7 of 7
Farin Urlaub
3
34points to level up
@farin-urlaub-2615
DS with strong interest in time series forecasting and nlp + llms

Active 82d ago
Joined Aug 23, 2023
Powered by