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AI's Memory Upgrade: Max Rumpf on how to build advanced RAG systems
Manage episode 428959169 series 3586305
I'm excited to share this conversation with Max Rumpf the founder of Sid.AI. I wanted to speak to Max because Retrieval Augmented generation has become core to building AI applications and he knows more about RAG than anyone I know.
We get deep into the challenges of building RAG systems and the episode is full of technical detail and practical insights.
We cover:
00:00 - Introduction to Max Rumpf and SID.ai
03:39 - How SID.ai's RAG approach differs from basic tutorials
07:30 - Challenges of document processing and chunking strategies
13:07 - Retrieval techniques and hybrid search approaches
15:06 - Discussion on knowledge graphs and their limitations
20:58 - Reranking in RAG systems and performance improvements
32:14 - Impact of longer context windows on RAG systems
35:10 - The future of RAG and information retrieval
39:47 - Recent research papers on AI and hallucination detection
42:04 - Value-augmented sampling for language model alignment
43:11 - Future trends and investment opportunities in AI
43:50 - SEO optimization for LLMs and its potential as a business
45:20 - Closing thoughts and wrap-up
I hope you enjoy the conversation and if you do, please subscribe!
25 つのエピソード
Manage episode 428959169 series 3586305
I'm excited to share this conversation with Max Rumpf the founder of Sid.AI. I wanted to speak to Max because Retrieval Augmented generation has become core to building AI applications and he knows more about RAG than anyone I know.
We get deep into the challenges of building RAG systems and the episode is full of technical detail and practical insights.
We cover:
00:00 - Introduction to Max Rumpf and SID.ai
03:39 - How SID.ai's RAG approach differs from basic tutorials
07:30 - Challenges of document processing and chunking strategies
13:07 - Retrieval techniques and hybrid search approaches
15:06 - Discussion on knowledge graphs and their limitations
20:58 - Reranking in RAG systems and performance improvements
32:14 - Impact of longer context windows on RAG systems
35:10 - The future of RAG and information retrieval
39:47 - Recent research papers on AI and hallucination detection
42:04 - Value-augmented sampling for language model alignment
43:11 - Future trends and investment opportunities in AI
43:50 - SEO optimization for LLMs and its potential as a business
45:20 - Closing thoughts and wrap-up
I hope you enjoy the conversation and if you do, please subscribe!
25 つのエピソード
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