Cover Image for The 2025 State of RAG
Cover Image for The 2025 State of RAG
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Presented by
TrustGraph
Graph powered context harness for AI agents
136 Went

The 2025 State of RAG

YouTube
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Past Event
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About Event

One year ago, Daniel Davis (TrustGraph) and Kirk Marple (Graphlit) sat down to dissect the hype around RAG with their 2024 State of RAG. They asked if long context windows would kill retrieval, debated the disappearance of Claude 3 Opus, and predicted that 2025 would be the year of "RAG-as-a-Service."

It’s time to check the scorecard.

Join us for the 2025 State of RAG, a no-fluff, deep-dive discussion into how the data infrastructure landscape has evolved. We are moving past the "Chat with PDF" demos and getting into the messy reality of production AI.

Daniel and Kirk will revisit their 2024 predictions and break down what is actually working in the enterprise today.

What We’ll Cover

  • The Prediction Scorecard: Kirk predicted 2025 would be the year of "As-a-Service" abstraction. Did the market move away from DIY LangChain spaghetti code?

  • The "Is RAG Dead?" Debate: With context windows growing, does retrieval still matter?

  • What's the future of MCP and interoperability?

  • Are ontologies the killer capability missing from RAG?

  • Is GraphRAG table stakes?

The Speakers

Daniel Davis | Co-Founder, TrustGraph
Coming from a background in heavy aerospace regulation and compliance, Daniel focuses on the precision of knowledge. He specializes in how AI interacts with complex ontologies and the "hard" problems of unstructured data.

Kirk Marple | Founder & CEO, Graphlit
A veteran of distributed systems and media management, Kirk approaches RAG as an ingestion and pipeline challenge. He is focused on the architecture required to turn raw data into queryable knowledge at scale.

Who Should Attend

  • AI Engineers and Data Architects

  • Founders building AI-native applications

  • Product Leaders navigating the "Build vs. Buy" infrastructure decision​​


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Avatar for TrustGraph
Presented by
TrustGraph
Graph powered context harness for AI agents
136 Went