The Hidden Layers of AI Systems: Memory, Retrieval, and Search
Most AI systems don't fail because of models. They fail because of memory, retrieval, and evaluation.
Agents accumulate too much context and lose signal. RAG pipelines degrade due to poor chunking. Search systems return results, but not always the right ones. And evaluating retrieval quality remains one of the hardest problems in production AI.
WHAT WE'LL EXPLORE
This meetup explores what it actually takes to build reliable, production-ready AI systems, with a focus on:
How agents store, retrieve, and forget information
How to improve context quality in RAG systems
How to evaluate search performance beyond simple metrics
How embeddings behave outside traditional text-based use cases
You'll hear from engineers working on real-world AI infrastructure, followed by Q&A and time to connect with other builders in Amsterdam.
TOPICS
Agentic Memory Architecture
Why memory matters in production AI systems. Avoiding context bloating and managing long-term memory. When agents should forget: memory lifecycle, drift, and degradation. Practical patterns using persistent memory systems.
Late Chunking in Production RAG Systems
Why naive chunking leads to poor retrieval. Moving from sentence-level embeddings to contextual chunking. Preserving document-level meaning across chunks. Improving recall and relevance in real-world systems.
Evaluating Search Quality
Why evaluation is often overlooked in retrieval systems. Comparing exact match vs semantic retrieval. Practical mental models for measuring search performance. Insights from recent research on retrieval evaluation.
Embeddings Beyond Text
Working with embeddings for code, tables, time-series, spatial, and image data. Retrieval patterns for non-text modalities. Real-world example: computer vision at the edge. Multi-camera product verification and semantic search across visual datasets.
AGENDA
5:30 PM - Doors open: Arrival, food, drinks, networking
6:15 PM - Welcome & kickoff
6:25 PM - Ewa Szyszka (Qdrant): Memory architecture, retrieval quality, and evaluation in production AI
7:05 PM - Speaker TBC
7:35 PM - Speaker TBC
8:05 PM - Panel discussion, audience Q&A, and networking
WHO SHOULD ATTEND
AI/ML engineers
Backend and data engineers
Engineers building RAG or agent systems
Technical founders and product builders
Anyone working on search, retrieval, or AI infrastructure
SPEAKERS
Ewa Szyszka - Qdrant
[Speaker TBC]
[Speaker TBC]