Today, we're joined by Jason Liu, freelance AI consultant, advisor, and creator of the Instructor library to discuss all things retrieval-augmented generation (RAG). We dig into the tactical and strategic challenges companies face with their RAG system, the different signs Jason looks for to identify looming problems, the issues he most commonly encounters, and the steps he takes to diagnose these issues. We also cover the significance of building out robust test datasets, data-driven experimentation, evaluation tools, and metrics for different use cases. We also touched on fine-tuning strategies for RAG systems, the effectiveness of different chunking strategies, the use of collaboration tools like Braintrust, and how future models will change the game. Lastly, we cover Jason’s interest in teaching others how to capitalize on their own AI experience via his AI consulting course - https://maven.com/indie-consulting/ai-consultant-accelerator?promoCode=TWIML
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📖 CHAPTERS
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00:00 - Introduction
2:38 - Tackling genAI challenges
6:44 - Evaluation loops, pipelines, and flywheels
9:50 - Building the data set
17:40 - Decision matrix for embedding implementation
23:25 - Evaluation tooling
26:29 - Compression rate
29:22 - Evaluation metrics
34:20 - Fine-tuning in RAG
36:34 - Long-context lengths
44:14 - Optimizations
48:51 - Multimodal
50:05 - Agentic programs
53:44 - AI consulting
🔗 LINKS & RESOURCES
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https://jxnl.co/
AI Consulting Course - https://maven.com/indie-consulting/ai-consultant-accelerator?promoCode=TWIML
Systematically Improving RAG Applications Course - https://maven.com/applied-llms/rag-playbook?promoCode=TWIML
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