Category · AI

RAG (Retrieval-Augmented Generation)

AI architecture that combines document retrieval with LLM generation.

Instead of training an LLM on your data, you feed it context retrieved on each query via search (BM25, vector DB, hybrid). Upside: grounded factuality, real-time updates, no expensive fine-tuning. Gotchas: indexing quality, retrieval latency, hallucinations on noisy context.

// In action with our clients

Relevant services

Turnkey delivery
Embedded squads

Client cases

/10European bank: hybrid RAG, inference cost cut tenfold.

IAAI agents in production: avoiding the demo theatre.
IARAG in production: from €10,000 to €900 per month.

// See also

Vector DB
Database optimized for ANN vector queries.
Embedding
Vector representation of a piece of text or media.
LLM (Large Language Model)
Large-scale language model trained on massive text corpora.
Fine-tuning
Continuing training of an LLM on a specific dataset.

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