SAG / ARCHITECTURE NOTE
What Is RAG? An Architecture That Connects Generative Answers to External Evidence
Explains the definition and need for RAG, how it works, criteria for applying it to SAG architecture, and a practical checklist, drawing on research and official documentation.
Definition in One Sentence
RAG is an architecture that retrieves external documents relevant to a question and provides them as input context to a generative model.
Key answer: Model parameters alone make it difficult to reliably provide up-to-date internal information and sources. Separating out the retrieval step makes it possible to trace which documents an answer is based on.
Why Is This Technology Needed?
Model parameters alone make it difficult to reliably provide up-to-date internal information and sources. Separating out the retrieval step makes it possible to trace which documents an answer is based on.
How It Works
The question is converted into a search representation, relevant documents are retrieved, and their rankings are adjusted before they are added to a limited context. The generated result is evaluated alongside the documents used.
Design should account for more than accuracy. Latency, cost, data boundaries, update frequency, and behavior in the event of failure must also be defined to produce reproducible results in operation. It is safer to leave values that automation cannot determine with confidence as unmeasured or requiring review, rather than converting them to 0 or marking them as successful.
Connection to SAG Technology
SAG has a path for page collection, normalization, and rule checks, as well as a report synthesis path restricted to evidence. When an LLM provider is configured, it validates responses that include evidence IDs. This should be distinguished from the RAG implementation in papers that use DPR and a vector index; that retrieval layer is a separate design and evaluation subject.
Practical Checklist
- Measure retrieval failures and generation failures separately
- Preserve the version and hash of evidence documents
- Check whether evidence supports each sentence in the answer
- Distinguish the status of failures, empty results, and permission errors from success
- Revalidate before and after changes under the same conditions
Research and Official Documentation
Reference documents provide a basis for principles and recommendations. They do not guarantee search visibility, AI mentions, rankings, or revenue. The actual impact of an implementation should be verified by observing service data under the same conditions.
Selection Criteria and a Specific Application Example
For a question about a product’s maintenance interval, first find the maintenance document for the relevant product and version. The fact that a document was included in an answer is not the same as every claim in the answer being supported by that document. Retrieval relevance and sentence-level evidence alignment must be reviewed separately.
Scope of Application at SAG
This article covers research principles and extended designs for search AI. Read it in connection with SAG’s page collection, evidence recording, and report validation structure, but do not interpret it to mean that all search algorithms from the papers have been incorporated into the operational pipeline. Whether they have been applied should be verified using the search module, evaluation data, and execution records.
Further Reading on This Technology
Compare RAG, GraphRAG, and Self-RAG papers and their application conditions.
SAG / KNOWLEDGE LINKS