Tolics Engineering Mind

How RLHF trains models to seek clarification instead of guessing — and a four-agent pipeline that brings the same discipline to your own requirements.

Most teams have no idea if their AI workflow investments are paying off. Here's what to actually measure instead of relying on vibes and excitement.

Context engineering — not prompt engineering — is where the real leverage lives. The discipline of deciding what goes into the context window, and why.

In hybrid AI organisations, developers aren't above the stack — they're stuck in between it, relaying intent like middle managers in a bloated org.

Your default environment shapes your baseline. Living near nature and visiting the city beats the reverse — your normal state should be the one that restores you.

Chat is the laziest AI integration. The real opportunity is components that remember users and act on context they've already provided.

A practical six-level AI maturity framework for companies adopting AI. Assess strategic clarity, data readiness, and engineering capability to find your gaps.

A-MEM's Zettelkasten-inspired approach to agent memory, and why software projects need federated memory with cross-domain linking — not one big vector database.

Fixed pipelines over-process simple requests and under-process complex ones. An adaptive router classifies intent and dispatches to specialised workflows.

AI-native is more than a buzzword. It marks a real architectural shift where AI becomes the foundation, not a feature bolted on.

Productivity culture destroys the spontaneous, unstructured time where creativity and connection actually happen. Become more human, not more efficient.

RAG pipelines leak PII through prompt injection. A substitution layer swaps real data for realistic fakes before it reaches the LLM, neutralising the attack.