16 posts
RAG (retrieval-augmented generation): how to make an assistant answer from your knowledge with a citation, instead of making things up. Architecture, quality, cost.
Documents, research, and client service make up 80% of a law firm's work—and that's where AI actually saves time. With confidentiality preserved.
Where AI actually boosts sales and reduces team workload in online stores — 24/7 support, offer personalization, product descriptions. No fluff.
How to maintain the relevance of a RAG knowledge base: strategies for incremental reindexing, document versioning, and knowledge drift detection in production environments.
An AI chatbot for a company website is more than just a response window. How to choose the approach, build on data, and avoid common implementation pitfalls.
How to choose a document chunking strategy for RAG in 2026: fixed size, recursive, semantic, tables, and code. Concrete sizes and overlap.
How to select an embedding model for RAG with Polish documents in 2026: criteria, comparison of multilingual and monolingual models, evaluation on your own data.
A corporate GPT on a knowledge base is an RAG assistant that responds using your documents. How to build it, what to ensure in the security layer, and when it pays off.
Hybrid search BM25 + vectors 2026: when semantics fails with SKU, how RRF fusion works, and how to configure hybrid search in a RAG system.
Models can confidently fabricate information. Here’s how to ensure your AI assistant responds based on facts and says 'I don’t know' instead of making things up.
Preparing data for AI is the foundation of every deployment: without clean, structured data, even the best model will respond poorly or hallucinate.
When fine-tuning makes sense: selection criteria, costs, and pitfalls. When RAG solves the problem cheaper, and when model training is the only way.
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.
What are embeddings and semantic search, how they work in practice, and when to implement them in a company knowledge base or product.
Why self-hosted LLM and RAG on your own knowledge give you control over cost, privacy, and provider.
Why a self-hosted language model simplifies GDPR compliance and what exactly changes in the flow of personal data.