Data, RAG & Self-Hosted AI tag | eYssen AI

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Data, RAG & Self-Hosted AI ×

embedding

Krisztian Eyssen

embedding

Text turned into a row of numbers, in which closeness of meaning becomes a measurable distance.

Beginner Everyone Glossary Data, RAG & Self-Hosted AI

Sep 13, 2026 Glossary

fine-tuning

Krisztian Eyssen

fine-tuning

Training a finished model further on your own examples so that it handles one narrow task better or more cheaply.

Beginner Everyone Glossary Data, RAG & Self-Hosted AI

Sep 13, 2026 Glossary

RAG (retrieval-augmented generation)

Krisztian Eyssen

RAG (retrieval-augmented generation)

The setup in which the model looks up the relevant passage in your own documents before it answers, and works from that.

Beginner Everyone Glossary Data, RAG & Self-Hosted AI

Sep 13, 2026 Glossary

vector database

Krisztian Eyssen

vector database

A store that holds vectors and quickly finds the ones most similar to the one you are looking for.

Beginner Everyone Glossary Data, RAG & Self-Hosted AI

Sep 13, 2026 Glossary

chunking

Krisztian Eyssen

chunking

Cutting documents into retrievable pieces — a bad split is the most common cause of bad answers.

Beginner Everyone Glossary Data, RAG & Self-Hosted AI

Sep 13, 2026 Glossary

Fine-Tuning: When Is It Finally Worth It?

Krisztian Eyssen

Fine-Tuning: When Is It Finally Worth It?

A fine-tuned model with 350 million parameters hit a **77.6%** success rate on a tool-calling task — while a general model with 175 billion parameters managed **26%** on the same task. Five hundred times fewer parameters, three times the result. That is the real promise of fine-tuning. And meanwhile one large provider is **retiring its fine-tuning platform**: no new users are being accepted, and existing ones have until January 2027.

Advanced Developer Explainer Data, RAG & Self-Hosted AI

Sep 13, 2026 Bite-Sized AI

Private AI: Keeping Data Inside Your Company

Krisztian Eyssen

Private AI: Keeping Data Inside Your Company

One large provider's zero data retention agreement lists item by item which endpoints it covers — and which it does not. On the does-not list: **file upload** and the **vector store**: **exactly the two features you would use to build RAG on that platform.** And on the other side, the assumption that "local means safe": a 293-day survey found **175,108 publicly reachable local model servers** in 130 countries.

Advanced Business owner How-To Data, RAG & Self-Hosted AI

Sep 13, 2026 Bite-Sized AI

Local AI Runners: Which One to Pick

Krisztian Eyssen

Local AI Runners: Which One to Pick

The most important thing to know: **Ollama, LM Studio, Jan, KoboldCpp and Docker Model Runner all use the same engine** — llama.cpp, and MLX on a Mac. They do not differ in speed but in the packaging and in the **defaults**. And the defaults are exactly where the trap is: Ollama **truncates at 4,096 tokens** out of the box, even when the model could handle 256k.

Beginner Developer Tool review Ollama Data, RAG & Self-Hosted AI

Sep 13, 2026 Bite-Sized AI

Quantization: How Big Models Fit on Small Machines

Krisztian Eyssen

Quantization: How Big Models Fit on Small Machines

According to a unified 2026 measurement, four-bit quantization **cuts model size by 71%** and costs on average **0.43 percentage points** of accuracy. Essentially free. Except **not for everyone**: the same four-bit quantization also produced a **59%** collapse on long input, and the degradation is **up to five times worse in languages other than English** — measured across 26 languages, Hungarian among them.

Advanced Developer Explainer Data, RAG & Self-Hosted AI

Sep 13, 2026 Bite-Sized AI

Hardware for Local AI: VRAM, RAM and Apple Silicon

Krisztian Eyssen

Hardware for Local AI: VRAM, RAM and Apple Silicon

The rule of thumb everyone repeats says you need about half the parameter count in memory. `[Own calculation]` The four-bit weights of an eight-billion-parameter model really do eat 4.6 gigabytes — **but the same model's 128k-token context eats 18 gigabytes**, four times the model itself. Anyone who only looks at model size has worked out a fifth of the memory they actually need. And there is one measurement that turns the whole picture around: **a chip with half the GPU cores processes a prompt 83% faster than a more expensive one** — while generating a fifth slower.

Advanced Developer How-To Data, RAG & Self-Hosted AI

Sep 13, 2026 Bite-Sized AI

Why Run AI on Your Own Machine?

Krisztian Eyssen

Why Run AI on Your Own Machine?

Of the four usual reasons, two hold up and two do not. `[Own calculation]` Against the cheapest API tier, a $3,500 machine breaks even at **2.6 billion tokens a year** — 133 days of continuous generation. And here is the twist that holds the whole article together: **the volume at which local running pays off is exactly the volume at which the local model is no longer good enough.** There is a fifth reason, though, that nobody states out loud, and measurably it is the strongest of the lot.

Beginner Business owner Explainer Data, RAG & Self-Hosted AI

Sep 13, 2026 Bite-Sized AI

Keeping the Knowledge Base Fresh

Krisztian Eyssen

Keeping the Knowledge Base Fresh

A measurement built on 96 thousand question-answer pairs and 219 thousand documents found that the mere presence of outdated content causes, according to the study, **at least a 20% drop in performance**, with some models doing worse than chance. But the most important finding is not the number: **models stay appropriately uncertain when they find nothing — and are confidently wrong when they find something stale.** An empty knowledge base is less dangerous than an old one.

Intermediate Business owner How-To Data, RAG & Self-Hosted AI

Sep 13, 2026 Bite-Sized AI

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