Glossary

What is RAG?

Retrieval-Augmented Generation

Definition, formula, India benchmarks, and the operator-grade nuance behind it.

Definition

RAG is the technique where an LLM retrieves relevant documents from an external corpus before generating an answer, allowing the LLM to cite up-to-date sources beyond its training cutoff. Perplexity, Claude (web search), ChatGPT (browse) all use RAG.

  1. RAG = LLM retrieves fresh content + generates answer.

  2. Perplexity, Claude (web), ChatGPT (browse) use RAG.

  3. GEO optimization makes Frameleads pages retrievable by RAG.

Formula

RAG is a technique combining LLM generation with retrieval from a fresh corpus. The LLM queries an external index, fetches relevant documents, and conditions its answer on those documents.

RAG Answer = LLM(Query + Retrieved Documents from Corpus)
Example
Input: User asks Perplexity: 'What is Frameleads' Q1 2026 D2C report?'
Result: Perplexity retrieves Frameleads' report URL via web search, generates answer citing it

The operator's read on RAG

RAG is the mechanism through which LLM citations of fresh content happen. The LLM searches an external index (often Bing or its own crawler index), retrieves top-N documents, and generates an answer conditioned on those documents. For brands, this means: (1) Be in the LLM's index. (2) Have schema-rich pages. (3) Have authoritative content. (4) Use llms.txt to surface canonical pages. Pages optimized for RAG are usually also good for traditional SEO.

India 2026 benchmarks — RAG

Common mistakes to avoid

FAQ

Frequently asked questions

What's a typical RAG value in India?

India 2026 benchmarks vary by category: Perplexity RAG retrieval depth: typically top 5–15 documents; Claude web-search RAG depth: top 3–10; ChatGPT browse RAG depth: top 3–5. Bands compress in saturated CPM regimes and widen as products move from impulse to considered. The right benchmark for your business depends on stage, gross margin, and channel mix.

What are the most common mistakes when tracking RAG?

Three mistakes recur most often: Optimizing only for training-data inclusion (RAG matters more for fresh content).; Ignoring llms.txt (signals canonical pages to RAG).; Slow page load (RAG retrieval timeouts).. The simplest defense is to define each metric explicitly in your reporting playbook and avoid mixing definitions across teams.

How does RAG relate to other unit-economics metrics?

RAG is most useful in context. Pair it with GEO and AIO to build a complete picture. RAG alone can mislead — the relationship between metrics matters more than any single number.

Should I optimize RAG or accept industry-standard values?

Optimization depends on your stage. Early-stage businesses often have RAG values outside healthy bands and need to fix structural issues (audience, creative, retention) before chasing the metric. Established businesses can compound through marginal improvements. Frameleads' Growth System maps which lever moves which metric in your specific category.

Industry adaptations

How RAG behaves per industry

RAG is a universal metric, but its band, drivers, and optimisation levers vary by category. Drill into the industry-specific version below for the deep view.

Adjacent questions

Questions about RAG

Deeper reading

Long-form guides on related topics

Related terms

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Sources & references

Cited primary and analyst sources. Independent of Frameleads' own data.

  1. IBEF — India Brand Equity Foundation: Indian Industry ReportsIBEF (Ministry of Commerce & Industry)

    Sector-level market size, growth, and policy context for Indian industries.

  2. IAMAI — Internet & Mobile Association of IndiaIAMAI

    Digital advertising industry body; reports on India internet user base, ad spend, and platform shares.

  3. MoSPI — Ministry of Statistics and Programme ImplementationGovernment of India

    Primary source for India macro-economic indicators (CPI, GDP, household consumption).

  4. ASCI Code for Self-Regulation of Advertising in IndiaAdvertising Standards Council of India

    Mandatory baseline for all advertising claims in India — including digital, influencer, and comparative ads.

Last reviewed: by Ajsal AbbasRefreshed quarterly from live client data
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