TECHNIQUE
Retrieval & Grounding
Basic RAG is deployed as a grounded retrieval layer over enterprise/domain corpora, with operators increasingly adding query scoping, hybrid retrieval, reranking, post-processing, and evaluation around the core retrieve-then-generate pattern.
Use retrieval to inject domain/source context into LLM answers or query handling, rather than relying on the model alone.
6 of 6 operators with quoted Basic RAG evidenceBack RAG with embeddings, vector stores, or vector databases for semantic retrieval.
3 of 6 operators with quoted Basic RAG evidenceConnect RAG to enterprise or product-specific knowledge sources such as wikis, docs, policies, job postings, handbooks, incident records, and root-cause analyses.
5 of 6 operators with quoted Basic RAG evidenceAdd query understanding, query optimization, or domain/source scoping before retrieval to improve relevance.
3 of 6 operators with quoted Basic RAG evidencePost-process, rerank, or restructure retrieved context before generation.
4 of 6 operators with quoted Basic RAG evidenceExpose grounded answers inside operational interfaces where users already work, especially Slack, chat, browser, mobile, or search experiences.
4 of 6 operators with quoted Basic RAG evidenceEvaluate grounded outputs for factuality, citation support, or production quality after deployment.
3 of 6 operators with quoted Basic RAG evidenceAcross the quoted deployments, Basic RAG is used to ground LLM behavior in operator-controlled domain data or retrieved source context.
The observed deployments treat RAG as an application pipeline component, not as a standalone model: retrieval is paired with prompt construction, answer generation, search/ranking, agents, or workflow execution.
Retrieval stack depth differs materially across operators.
APPROACH 01
Basic vector-database or embedding-backed retrieval is the explicit RAG store/retriever.
APPROACH 02
Hybrid or filtered retrieval combines vector search with BM25 or source/document narrowing.
APPROACH 03
Rerankers or explicit ranking stages reduce or order retrieved context before downstream use.
RAG is deployed in different product surfaces and operating workflows.
APPROACH 01
Internal no-code app builder with Knowledge Vault lookups and user-provided knowledge sources.
APPROACH 02
Search/query engine that uses RAG context and embeddings to interpret natural-language job-search intent.
APPROACH 03
Operational support or security workflows that generate triage, summaries, verdicts, or Slack answers from retrieved context.
APPROACH 04
Multi-agent architecture where dedicated RAG agents retrieve unstructured sources under a supervisor agent.
Operators differ in how much explicit human or automated quality control surrounds RAG outputs.
APPROACH 01
Automated regression, staging, production sampling, judge-model, and manual spot-check evaluation around the RAG pipeline.
APPROACH 02
Human reviewer validates final generated incident reports before publishing, and incomplete-context cases are escalated for human review.
APPROACH 03
Tracing, layered evals, and production monitoring are part of the production agent/RAG system.
Hallucination, misinformation, and factual-support failures remain explicit concerns even when retrieval is present.
Ambiguous or underspecified queries force extra query optimization, source identification, intent classification, or human escalation.
Raw semantic retrieval is often not enough; operators report needing ranking, reranking, BM25, document narrowing, or context post-processing to improve relevance.
| Name | Kind | When | Maturity |
|---|---|---|---|
| pgvector | library | embeddings should live beside existing Postgres data with one operational store | commodity |
| Qdrant | service | dedicated vector search with payload filtering at scale | established |
| LlamaIndex | library | fastest path to ingestion plus retrieval scaffolding in one framework | established |