How Elasticsearch Improves Search Relevance, Log Parsing, Production Systems, + More! Modern Web podcast

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How Elasticsearch Improves Search Relevance, Log Parsing, Production Systems, + More!

4d ago 35:34

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In this episode of the Modern Web Podcast, Rob Ocel and Danny Thompson talk with Philipp Krenn, Head of Developer Advocacy at Elastic, about how Elasticsearch has evolved from a search engine into a foundation for observability, security, and AI-powered systems. Philipp explains how Elastic approaches information retrieval beyond just vector search, using tools like LLMs for smarter querying, log parsing, and context-aware data access.

They also discuss how Elastic balances innovation with stability through regular releases and a focus on long-term reliability. For teams building with AI, Elastic offers a way to handle search, monitoring, and logging in one platform, making it easier to ship faster without adding complexity.

Key points from this episode:

Elasticsearch has expanded beyond search to support observability and security by treating all of them as information retrieval problems.

Elastic integrates with AI tools like LLMs to improve search relevance, automate log parsing, and enable features like query rewriting and retrieval-augmented generation.

Vector search is just one feature in a larger toolkit for finding relevant data, and Elastic supports hybrid and traditional search approaches.

Elastic maintains a steady release cadence with a focus on stability, making it a reliable choice for both fast-moving AI projects and long-term production systems.

Philipp Krenn on Linkedin: https://www.linkedin.com/in/philippkrenn/

Rob Ocel on Linkedin: https://www.linkedin.com/in/robocel/

Danny Thompson on Linkedin: https://www.linkedin.com/in/dthompsondev/

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