๐ Advancing AI: Synthetic Data Generation for Robust RAG Evaluation ๐I've been exploring the fascinating world of synthetic data generation (SDG) and its potential for evaluating and improving RAG pipelines.
#LangChain#LLMs#RAGAS#RAG#AI
1/5
2. The Evol instruct methodology for SDG, including in-depth and in-breadth techniques, is incredibly powerful. I particularly enjoyed using prompts to generate data and enhance RAG applications.
4/5
๐ Just wrapped up AIE5 Assignment 6 - Multi-Agent Applications with LangGraph!
Explored multi-agent collaboration, RAG chains integration, and helper functions. Still diving into advanced features and real-world applications. Excited to keep learning!
Part 1/2
๐ Just completed Agentic RAG! ๐
Check out my GitHub repo for all the details: https://t.co/ZePvJLp22B
๐ฅ Watch my Loom video walkthrough: https://t.co/vrcNwrFZ2W
@AIMakerspace
Feel free to reach out if you're curious or would like to collaborate on similar projects! ๐ค๐ฅ
I've built "The Dinking Forecast" โ an AI-powered pickleball assistant that helps you find the perfect time to play based on weather conditions.
"The Dinking Forecast" analyzes factors like temperature, wind, and humidity to provide personalized playing window recommendations.
Dive deep into the world of logits, softmax, and decoding techniques. Learn how to fine-tune your models with parameters like temperature, top-p, and top-k. And discover the importance of guardrails for safe and ethical AI.
https://t.co/5rGiUUjTx4
@AIMakerspace#LLM