Knowledge Base QA (RAG)

2026-grade enterprise RAG: Adaptive RAG routes by query complexity, Hybrid (vector + BM25) retrieval feeds a cross-encoder reranker, GraphRAG handles multi-hop relations, hallucinations are auto-suppressed.

<5%

Hallucination Rate

>120%

NRR Lift

95%

Answer Accuracy

50ms

Retrieval Latency

Challenge

Pain points in traditional RAG systems

Hallucinations

Model drift generates false information, severely damaging user trust

Stale Knowledge

Knowledge base updates lag, unable to reflect latest information

Context Loss

Poor retrieval relevance leads to inaccurate answers

Scaling Issues

Retrieval efficiency drops dramatically as data grows

System Architecture

Solution

SkyAIApp Enterprise RAG Platform

Hybrid retrieval + reranker

Vector + BM25 push recall@k to 95%+; a cross-encoder reranker then sharpens precision — 15-25% accuracy lift over vector-only on most enterprise corpora.

Adaptive RAG routing

A classifier decides per query: simple lookup → cheap pipeline, multi-hop → Agentic RAG, fresh facts → live tool call. Right complexity at the right cost.

GraphRAG for multi-hop relations

Supply-chain, legal-clause, and medical-comorbidity questions traverse a graph. Adding GraphRAG selectively raises recall@k by 30+ points on relation-heavy domains.

Hallucination suppression + citations

Answers must carry citations; we cross-check claim vs source. Low-confidence answers fall back to Claude Opus 4.7 or human handoff.

Incremental knowledge updates

Vector, BM25 and graph layers all accept sub-second incrementals — no full re-index ever.

Pluggable vector stores

Pinecone / Weaviate / pgvector / Milvus / Qdrant behind one SDK with per-namespace gradual rollout.

Modeled Results

<5%

Hallucination Rate

Multi-layer validation significantly reduces false information

>120%

NRR Lift

Accurate answers improve retention and conversion

95%

Answer Accuracy

Hybrid retrieval ensures high-quality answers

50ms

P95 Retrieval Latency

Optimized retrieval meets real-time requirements

Retrieval stack

Pinecone

Vector · cloud-native

Weaviate

Vector · hybrid search

pgvector + BM25

Postgres-native

Qdrant / Milvus

Self-hosted distributed

Cohere Rerank 3

Cross-encoder reranker

Neo4j / Memgraph

GraphRAG knowledge graphs

Composite profile — Lumen Education-style K-12 platform

This composite profile models a 3.2M-student K-12 platform where vector-only RAG can reach an 18% hallucination rate on STEM questions. The SkyAIApp Adaptive RAG replay benchmark shows hallucinations down to 4.7%, repeat-question consistency up from 62% to 96%, and every answer carrying traceable citations.

Key call: simple factual questions go BM25 + Haiku 4.5; multi-step reasoning (e.g. geometry proofs) escalates to Adaptive RAG + Sonnet 4.6; lab-data questions trigger GraphRAG over the curriculum graph. Any low-confidence answer auto-falls back to Opus 4.7 for re-review.

Composite-profile quote: “The valuable thing is not the low hallucination number alone; it is that every answer is citation-clickable and fast to verify.”

Tech stack

Vector storepgvector (Postgres)
BM25Elasticsearch 9
RerankerCohere Rerank 3
GraphNeo4j (curriculum)
GeneratorsHaiku 4.5 / Sonnet 4.6 / Opus 4.7
EmbeddingsVoyage-3-large

Implementation timeline

Week 1

Data ingestion

Index curriculum into pgvector + ES; build first-pass Neo4j graph.

Week 2

Adaptive routing

Label 200 sample queries to train the classifier; tune thresholds.

Week 3

Hallucination suppression

Make citations mandatory + cross-check rules; wire low-confidence fallback.

Week 4

Launch review

Complete full-rollout review; subscribe router.fallback_triggered alerts.

Adaptive RAG configuration

import { SkyAI } from "@skyaiapp/sdk";

const sky = new SkyAI({ apiKey: process.env.SKYAIAPP_API_KEY! });

// 1) Classify query complexity (cheap call)
const cls = await sky.route({
  goal: "cost", strategy: "cost-optimized",
  models: ["claude-haiku-4.5"],
  messages: [{ role: "user", content: `Classify: simple-fact | multi-step | data-lookup\nQ: ${q}` }],
  budget: { maxCostUsd: 0.0003 },
});

// 2) Route to the right pipeline
if (cls.output === "simple-fact") {
  // BM25 + small generator. Fast & cheap.
  return sky.route({
    goal: "stability", strategy: "balanced",
    models: ["claude-haiku-4.5"],
    rag: { source: "bm25:lumen-curriculum", topK: 5, requireCitations: true },
    fallback: { models: ["gpt-5.5-instant"] },
  });
}

if (cls.output === "multi-step") {
  // Hybrid retrieval + reranker + Sonnet 4.6
  return sky.route({
    goal: "quality", strategy: "quality-first",
    models: ["claude-sonnet-4.6"],
    rag: {
      source:    "hybrid:lumen-curriculum",     // pgvector + BM25
      reranker:  "cohere-rerank-3",
      topK:      8, rerankTopK: 3,
      requireCitations: true,
    },
    confidenceFallback: {
      below: 0.85,
      to:    { models: ["claude-opus-4.7"] },   // re-answer with Opus
    },
  });
}

// data-lookup → traverse the curriculum graph (Neo4j) for multi-hop questions.
return sky.route({
  goal: "quality", strategy: "balanced",
  models: ["claude-sonnet-4.6"],
  rag: {
    source: "graph:neo4j-lumen",
    graphHops: 3,
    requireCitations: true,
  },
  fallback: { models: ["gpt-5.5-pro"] },
});

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