Artificial Intelligence on Nalar

Evaluating RAG Systems with a Small Golden Dataset

Tue, 01 Sep 2026 00:00:00 +0700

Retrieval-augmented generation (RAG) is easy to demo and surprisingly hard to evaluate. A fluent answer can hide weak retrieval, while a good retriever can be blamed for an answer model that ignores its evidence.

A useful evaluation process separates those failure modes. You do not need thousands of examples to begin. A carefully maintained golden dataset of 30 to 100 representative questions can catch many regressions before users do.

Define what the system is supposed to do

Start with the product contract rather than a model metric. For a documentation assistant, useful requirements might be:

Validate LLM Output with Structured Contracts

Tue, 01 Sep 2026 00:00:00 +0700

Large language models are useful when software needs to turn ambiguous text into a structured decision, extraction, or plan. The dangerous shortcut is to treat a model response as if it were already trusted application data.

Even when a provider can constrain output to JSON or a schema, the result can still be semantically wrong: a date can be impossible, an identifier can refer to a nonexistent record, or a supposedly positive amount can be negative. Reliable integrations therefore need a contract boundary between model output and the rest of the system.

Version Embeddings for Safe Semantic Search Migrations

Tue, 01 Sep 2026 00:00:00 +0700

Semantic search systems often look simple from the outside: encode a document, store its vector, encode a query, and compare the vectors. The operational difficulty appears later, when the embedding model changes.

Two models can produce vectors with the same dimension and still define completely different coordinate spaces. Mixing vectors from model A with query vectors from model B can silently destroy ranking quality without producing an obvious error.

The safe approach is to treat an embedding model as a versioned data dependency, not a drop-in function.