AI architecture and LLM systems by Softlance

RAG — retrieval-augmented generation — retrieves the relevant parts of your own documents and gives them to the model as context, so answers are grounded in your data and can cite their source. You need it whenever answers must reflect information the model was never trained on: internal documentation, product catalogues, contracts, support histories.

RAG for knowledge that changes or must be cited; fine-tuning for format, tone and task-specific behaviour that prompting alone cannot pin down. Most production systems that need both start with RAG, because it is cheaper to update and far easier to audit.

You constrain and verify rather than hope. Grounding answers in retrieved context, requiring citations, structured output schemas, confidence thresholds that route uncertain cases to a human, and a scored evaluation set that catches regressions before release.

It depends on volume and model choice, and it is very often the design rather than the provider that decides the bill. We model cost per request during design and cut it with prompt caching, smaller models for easy paths, batching and result caching — commonly by an order of magnitude against a naive implementation.