RAG Versus Fine Tuning-How to Efficiently Tailor an LLM to Your Domain Data

🚀 Ready to supercharge your enterprise use cases with cutting-edge techniques? Join us in this deep dive with Malte Pietsch, co-founder and CTO at deepset, as he unravels the intricacies of integrating domain knowledge into Language Model (LLM) applications.
🧠 Discover the dynamic duo of Retrieval-augmented generation (RAG) and fine-tuning, the two key methods revolutionizing the landscape of LLM application development. Malte shares invaluable insights on selecting the method that aligns with your use case, and walks you through the essential steps to optimize performance, control costs, and minimize latency before taking your project to production.
🔍 What to expect:
- Insightful breakdown of RAG and fine-tuning methods for effective integration of domain knowledge.
- Practical tips for evaluating and enhancing the performance of RAG pipelines.
- Hands-on guidance from Malte Pietsch, a seasoned expert with a wealth of experience in NLP research and application development.
💡 About Malte Pietsch:
Malte is the co-founder and CTO at deepset, driving innovation in LLM applications through projects like Haystack and deepset Cloud. With a background in NLP research at Carnegie Mellon University and extensive experience as a data scientist in various startups, Malte has been at the forefront of crafting NLP applications for over eight years. Learn from his expertise on navigating development workflows and prioritizing user orientation for successful NLP/LLM projects.
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Website: www.deepset.ai
🚀 Elevate your AI game with insights from the experts. Let's shape the future of building LLM applications together! 🌐

[@adityavipradas3252]

Great explanation.