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In this episode of "How AI is Built", we learn how to build and evaluate real-world language model applications with Shahul and Jithin, creators of Ragas. Ragas is a powerful open-source library that helps developers test, evaluate, and fine-tune Retrieval Augmented Generation (RAG) applications, streamlining their path to production readiness.
Main Insights
Practical Takeaways
Interesting Quotes
Ragas:
Jithin James:
Shahul ES:
Nicolay Gerold:
00:00 Introduction
02:03 Introduction to Open Assistant project
04:05 Creating Customizable and Fine-Tunable Models
06:07 Ragas and the LLM Use Case
08:09 Introduction to Language Model Metrics (LLMs)
11:12 Reducing the Cost of Data Generation
13:19 Evaluation of Components at Melvess
15:40 Combining Ragas Metrics with AutoML Providers
20:08 Improving Performance with Fine-tuning and Reranking
22:56 End-to-End Metrics and Component-Specific Metrics
25:14 The Importance of Deep Knowledge and Understanding
25:53 Robustness vs Optimization
26:32 Challenges of Evaluating Models
27:18 Creating a Dream Tech Stack
27:47 The Future Roadmap for Ragas
28:02 Doubling Down on Grid Data Generation
28:12 Open-Source Models and Expanded Support
28:20 More Metrics for Different Applications
RAG, Ragas, LLM, Evaluation, Synthetic Data, Open-Source, Language Model Applications, Testing.
In this episode of "How AI is Built", we learn how to build and evaluate real-world language model applications with Shahul and Jithin, creators of Ragas. Ragas is a powerful open-source library that helps developers test, evaluate, and fine-tune Retrieval Augmented Generation (RAG) applications, streamlining their path to production readiness.
Main Insights
Practical Takeaways
Interesting Quotes
Ragas:
Jithin James:
Shahul ES:
Nicolay Gerold:
00:00 Introduction
02:03 Introduction to Open Assistant project
04:05 Creating Customizable and Fine-Tunable Models
06:07 Ragas and the LLM Use Case
08:09 Introduction to Language Model Metrics (LLMs)
11:12 Reducing the Cost of Data Generation
13:19 Evaluation of Components at Melvess
15:40 Combining Ragas Metrics with AutoML Providers
20:08 Improving Performance with Fine-tuning and Reranking
22:56 End-to-End Metrics and Component-Specific Metrics
25:14 The Importance of Deep Knowledge and Understanding
25:53 Robustness vs Optimization
26:32 Challenges of Evaluating Models
27:18 Creating a Dream Tech Stack
27:47 The Future Roadmap for Ragas
28:02 Doubling Down on Grid Data Generation
28:12 Open-Source Models and Expanded Support
28:20 More Metrics for Different Applications
RAG, Ragas, LLM, Evaluation, Synthetic Data, Open-Source, Language Model Applications, Testing.