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Notes from the lab.

Notes from the lab: agents, sovereign infrastructure, data, visibility and PropTech.

Page 12 of 14 · 164 posts

AI Agents01/06/2026

AI implementation plan step by step: first 30 days

Concrete AI implementation plan for the first 30 days: from process audit through pilot to measurable results. No hype, just numbers.

AI Research01/06/2026

AI Black Box: How Explainability and Guardrails Protect Companies

The AI black box problem poses real legal and operational risks. How XAI, guardrails, and human oversight address it in production systems compliant with the AI Act.

AI Infrastructure01/06/2026

Prompt caching in LLMs: how a cheaper static prefix cuts bills

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.

AI Infrastructure01/06/2026

Prompt engineering for businesses: what works and what doesn’t

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.

AI Agents01/06/2026

Prompt injection: how to protect your corporate AI assistant

A malicious instruction in content can hijack an AI assistant. What prompt injection is and how we build defenses before something goes wrong.

AI Infrastructure01/06/2026

RAG: how to evaluate response quality (golden set)

RAG evaluation step by step: golden set, faithfulness and relevance metrics, LLM-as-judge, regression tests, and AI Act audit trail for RAG systems.

AI Agents01/06/2026

RAG vs fine-tuning: how to give a model your company’s knowledge

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.

AI Infrastructure01/06/2026

Reranking: how to improve search quality in RAG

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.

AI Research01/06/2026

Human in the AI loop: when oversight saves the decision

Why human oversight isn't a brake on automation but its condition. Human-gate, explainability, and AI Act in one architecture.

AI Research01/06/2026

Algorithmic bias: how to prevent transferring prejudices into AI systems

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 Agents01/06/2026

Multi-agent systems in a company: when to use them and how to connect them

AI multi-agent systems 2026: when orchestrating multiple specialized agents outperforms a single overloaded one and how to avoid loops, costs, and chaos.

AI Infrastructure01/06/2026

Company data and AI: data processing agreement and legal basis

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.

Concrete takeaways on AI agents, sovereign infrastructure and visibility in AI models — no spam.

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Notes from the lab.

AI implementation plan step by step: first 30 days

AI Black Box: How Explainability and Guardrails Protect Companies

Prompt caching in LLMs: how a cheaper static prefix cuts bills

Prompt engineering for businesses: what works and what doesn’t

Prompt injection: how to protect your corporate AI assistant

RAG: how to evaluate response quality (golden set)

RAG vs fine-tuning: how to give a model your company’s knowledge

Reranking: how to improve search quality in RAG

Human in the AI loop: when oversight saves the decision

Algorithmic bias: how to prevent transferring prejudices into AI systems

Multi-agent systems in a company: when to use them and how to connect them

Company data and AI: data processing agreement and legal basis

Newsletter

Notes from the lab.

AI implementation plan step by step: first 30 days

AI Black Box: How Explainability and Guardrails Protect Companies

Prompt caching in LLMs: how a cheaper static prefix cuts bills

Prompt engineering for businesses: what works and what doesn’t

Prompt injection: how to protect your corporate AI assistant

RAG: how to evaluate response quality (golden set)

RAG vs fine-tuning: how to give a model your company’s knowledge

Reranking: how to improve search quality in RAG

Human in the AI loop: when oversight saves the decision

Algorithmic bias: how to prevent transferring prejudices into AI systems

Multi-agent systems in a company: when to use them and how to connect them

Company data and AI: data processing agreement and legal basis

Newsletter