Notes from the lab: agents, sovereign infrastructure, data, visibility and PropTech.
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OWASP LLM Top 10 outlines 10 vulnerability classes in large language models. How each manifests in production systems and how to build layered defenses.
LLM semantic cache in 2026: how the embedding similarity threshold works, when it reduces costs by 40-60%, what risks it carries, and how to manage invalidation.
An AI chatbot for a company website is more than just a response window. How to choose the approach, build on data, and avoid common implementation pitfalls.
How to choose a document chunking strategy for RAG in 2026: fixed size, recursive, semantic, tables, and code. Concrete sizes and overlap.
AI achieves physician-level precision in narrow diagnostic tasks, but without explainability, oversight, and AI Act compliance, it is unfit for standalone clinical decisions.
What synthetic data is, when it replaces real data in AI training and testing, how to generate it in compliance with GDPR and AI Act, and which risks to control.
Three leading model families, three distinct profiles. Head-to-head based on measured parameters—and when to choose which.
Learn the 7 main reasons AI projects fail in companies: from poor data and lack of guardrails to ignoring GDPR and the AI Act. Find out how to eliminate them.
How to select an embedding model for RAG with Polish documents in 2026: criteria, comparison of multilingual and monolingual models, evaluation on your own data.
How to test an AI agent before deployment in 2026: golden sets, faithfulness, tool accuracy, regression tests, and the limits of LLM-as-judge.
A corporate GPT on a knowledge base is an RAG assistant that responds using your documents. How to build it, what to ensure in the security layer, and when it pays off.
Hybrid search BM25 + vectors 2026: when semantics fails with SKU, how RRF fusion works, and how to configure hybrid search in a RAG system.
Concrete takeaways on AI agents, sovereign infrastructure and visibility in AI models — no spam.
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