AI
Production-ready answers on AI in real systems.
What AI covers
This category covers building AI features for production — from RAG and LLM integration to cost, limits and operations.
What this category covers
- RAG (retrieval-augmented generation) explained
- RAG vs. fine-tuning
- A production RAG pipeline
- Estimating LLM costs
Solid answers in this field.
In-depth, citable documents — every recommendation with its trade-off, its cost and the case in which we decide differently.
AI in Production Systems — Engineering, Not Demos
The long road from an AI demo to a reliable production system: evaluation, failure modes, guardrails — wrapping the probabilistic in the deterministic.
When AI Is the Wrong Solution
From a company that builds AI: when it is the wrong choice. What shape a problem needs, why a rule is often superior, what AI costs in the wrong place.
Reliable AI Answers — Architecture for Context and Sources
How to ground an AI answer in reliable, current context and traceable sources so that it is correct and verifiable — not merely fluent.
Agentic Coding — When Software Development Becomes Orchestration
Agentic coding shifts the bottleneck of software development: from writing the code to defining, verifying and taking responsibility for the work.
Lessons learned in this field.
Mistakes and what they taught us — anonymized, without drama. The mistake is the teacher.
New articles in this category are published on an ongoing basis. You can already find the core terms in the glossary.
Go to the glossaryA concrete problem in this field?
We don't just solve it on paper. Talk to the engineers who would build it.
