Coffee With Developers • Aug 31, 2026

AI Vector Search at Scale - Ewa Szyszka - Ewa Szyszka

Ewa Szyszka

Ewa Szyszka argues the strongest AI founders combine technical fluency with deep domain expertise. Learn to scale vector search, slash LLM costs, and deploy robust agentic evaluations.

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#1 about 2 min

Optimizing token usage with vector search retrieval

Efficient information retrieval using vector databases prevents hitting rate limits and reduces token costs.

#2 about 2 min

Preparing and chunking data for vector databases

Understanding use cases and properly chunking data before embedding ensures highly relevant search results.

#3 about 2 min

Scaling search and managing multimodal data types

Vector databases handle large-scale multimodal data like audio and video using algorithms like HNSW and TurboQuant.

#4 about 2 min

Bridging customer needs and technical solutions in developer relations

Developer relations engineers build practical workshops based on real-world customer stories and ecosystem demands.

#5 about 2 min

Exploring multimodal AI applications beyond standard language models

Processing unstructured data like CCTV footage and audio opens new possibilities for anomaly detection and advanced analytics.

#6 about 2 min

Managing dirty data and continuous agentic evaluations

Implementing continuous evaluation processes with golden answer datasets is critical for handling real-world data errors in production.

#7 about 2 min

Comparing startup founder experiences with developer relations roles

Building in public as a founder or advocate sharpens technical skills and fosters rapid continuous learning.

#8 about 3 min

Combining domain expertise with technical skills for AI startups

Founding successful AI companies requires pairing technical fluency with deep domain knowledge and delegating specialized tasks.

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