RAG Command Center · Diverse Industries Inc

📊 Unified Dashboard

Your current RAG strategy at a glance — pattern, readiness, cost, and pipeline priorities.

🎯 Recommended Pattern

—

Run the Recommender to get a suggestion.

📋 Readiness Score

—

Check your readiness above.

Prototype

💰 Estimated Monthly Cost

—

Adjust sliders in the Estimator tab.

⚡ Priority Pipeline Steps

Select a pattern to see priorities.

💡 Quick advice: Run the Recommender to get started.

📋 The 10 RAG Patterns

Click any pattern to expand — see use cases, trade‑offs, and when to use it.

🎯 Pattern Recommender

Answer 3 questions — get a tailored RAG pattern recommendation.

💰 Budget / Cost

🎯 Accuracy need

📁 Data complexity

—

Your recommendation will appear here.

confidence: —

🔍 Pipeline Explorer

Click a step to see best practices, tools, and pitfalls — straight from the blueprint.

📄 Ingestion

✂️ Chunking

🔢 Embeddings

🗄️ Vector DB

🔍 Retrieval

🧠 Generation

Select a step above

Click on any pipeline step to see detailed guidance.

🏗️ Pattern Architecture View

Select a pattern to see which pipeline steps are critical (high/medium/low priority).

Choose a pattern:

Ingestionmedium

Chunkinghigh

Embeddingshigh

Vector DBmedium

Retrievalhigh

Generationmedium

📊 Quick Comparison

Cost, latency, complexity, and best fit for each pattern.

Pattern	Cost	Latency	Complexity	Best for

⚡ Query Simulator

Type a question — see which RAG pattern would be triggered and why.

💡 Your simulated RAG decision will appear here.

📋 RAG Readiness Checklist

Evaluate your organization's readiness to deploy RAG in production.

Data is digitized and clean — structured or well‑formatted text with minimal noise.

Data includes complex structures — tables, images, or mixed‑content PDFs.

Clear query use‑case defined — you know exactly what questions users will ask.

Budget for embedding storage & compute — vector DB costs are accounted for.

Low latency requirement (< 2s) — users expect real‑time responses.

High accuracy & fact‑checking needed — hallucinations are not acceptable.

0 / 6 · Prototype

Start with a simple RAG prototype and iterate.

💰 Cost & Latency Estimator

Estimate monthly RAG costs and average latency based on your scale.

Number of documents 1,000

Avg tokens per document 5,000

Queries per day 1,000

$4.50

Embedding / mo

$18.00

Generation / mo

$2.40

Vector DB / mo

$24.90

Total monthly

1.4s

Avg latency

📖 Interactive Glossary

Click any term for a plain‑English definition pulled from the RAG blueprint.

Chunking

Breaking documents into pieces. Semantic chunking splits at topic shifts; hierarchical uses small + parent chunks for context.

Embeddings

Numerical vectors that represent the meaning of text. Used for semantic search to find relevant chunks.

Vector DB

Database optimized for storing and querying embeddings. Supports hybrid search (keyword + semantic).

Hybrid Search

Combines keyword matching (BM25) and semantic (vector) search. Outperforms either alone in production.

HyDE

Hypothetical Document Embeddings — the LLM generates a fake answer first, then uses that to search, bridging the query‑document gap.

CRAG

Corrective RAG — adds a quality gate after retrieval. If docs are low‑quality, it reformulates the query or falls back to web search.

Self‑RAG

The model generates reflection tokens to critique its own retrieval and reasoning in real‑time, ensuring high accuracy.

Agentic RAG

LLM acts as an orchestrator, deciding when to search, call APIs, or run code, looping until a sufficient answer is formed.

Graph RAG

Builds a knowledge graph over entities and relationships. Excels at multi‑hop questions that require connecting dots across documents.

Multimodal RAG

Uses vision models to generate text descriptions for images and tables at ingestion, making them searchable via text embeddings.