Notes from the lab: agents, sovereign infrastructure, data, visibility and PropTech.
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LLM prompt caching in 2026: what is a static prefix cache, how it differs from semantic cache, and how to structure your prompt to hit the cache.
Prompt engineering for businesses in 2026: techniques that improve LLM quality, mistakes that waste tokens and time, guardrails, GDPR and AI Act in prompt design.
A malicious instruction in content can hijack an AI assistant. What prompt injection is and how we build defenses before something goes wrong.
RAG evaluation step by step: golden set, faithfulness and relevance metrics, LLM-as-judge, regression tests, and AI Act audit trail for RAG systems.
Two paths to a model that knows your business. When RAG is enough, when fine-tuning is needed—and why RAG is usually the answer.
What is reranking in RAG, when a cross-encoder beats ANN, and how to build a search pipeline that returns relevant chunks instead of just similar ones.
Why human oversight isn't a brake on automation but its condition. Human-gate, explainability, and AI Act in one architecture.
Where algorithmic bias comes from, how to measure and mitigate it at every stage: from data through model to deployment. A practical guide from the 2026 perspective.
AI multi-agent systems 2026: when orchestrating multiple specialized agents outperforms a single overloaded one and how to avoid loops, costs, and chaos.
When using AI requires a data processing agreement (DPA), what it must include, and how to avoid legal gaps when implementing an assistant or automation.
A model that sees. Vision AI reads documents, describes photos, and extracts data from images — where it actually saves hours.
Voice or text? Not a competition, but two channels with different strengths. When to choose which—and when to use both.
Concrete takeaways on AI agents, sovereign infrastructure and visibility in AI models — no spam.
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