When was RAG invented? The RAG approach, roadmaps, agentic RAG and RAG status

Retrieval-augmented generation (RAG) was introduced in 2020 in a paper by Patrick Lewis and colleagues at Facebook AI Research and partner universities, which combined a neural retriever with a text generator so answers could draw on an external knowledge source. Today RAG is the standard way to ground language models in your own documents. I built RAG.NextUpgrad, a production RAG platform, so this page covers RAG's origin and definition, the approach, why it and embeddings matter, roadmaps for RAG pipelines, chatbots and agentic RAG, and the unrelated RAG assessment in project management.

When was RAG invented?

RAG was introduced in 2020 by Lewis and colleagues in the paper "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks", which showed that retrieving Wikipedia passages and generating answers from them improved factual tasks compared with models relying only on their parameters.

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The idea built on earlier work in open-domain question answering, such as dense passage retrieval, and on decades of information retrieval research. The term became widespread after 2022, when large language models such as ChatGPT made it practical to build RAG applications with an off-the-shelf model and a vector index. Modern RAG differs from the original paper, which trained retriever and generator together; most systems today combine separate components.

What is the definition of RAG?

In AI, RAG stands for retrieval-augmented generation: a technique where a system retrieves relevant information from external sources and supplies it to a language model so the model's answer is grounded in that information. In project management, RAG means Red, Amber, Green status ratings.

Also asked as: do rag definition

The AI definition has three parts: retrieval, finding relevant passages from documents, databases or the web; augmentation, adding those passages to the model's prompt; and generation, producing the answer, ideally with citations. RAG lets models use private, recent or specialised knowledge without retraining, and makes answers verifiable.

What is the RAG approach?

The RAG approach is to retrieve first, then generate: search a knowledge source for passages relevant to the question, place the best ones in the prompt with instructions to answer only from them, and have the model write a cited answer, declining if the evidence is insufficient.

Also asked as: what is rag approach

A production RAG approach adds layers: clean ingestion and chunking, hybrid retrieval with keyword and vector search, reciprocal-rank fusion, a reranker, prompt rules for citations, a confidence threshold for refusing weak-evidence answers, and evaluation. This is the shape of RAG.NextUpgrad, which also streams answers and falls back across model providers. The approach keeps knowledge in documents you control rather than in the model's weights.

Why is RAG important?

RAG is important because it makes language models useful for real organisations: answers can use private and current information, cite sources, and be updated by changing documents, while reducing hallucinations compared with relying on the model's memory.

It also lowers cost and risk compared with fine-tuning for knowledge, supports access control at retrieval time, and works with smaller models when retrieval is good. Most enterprise AI assistants, from customer support bots to internal knowledge search, are built on RAG. For engineers, RAG skills, retrieval, evaluation and grounding, are among the most requested in GenAI roles.

Why are embeddings important in RAG?

Embeddings make semantic retrieval possible: they represent the meaning of questions and passages as vectors, so the system can find relevant passages even when wording differs. Without good embeddings, a RAG system misses answers phrased differently from the question.

Embedding quality directly affects retrieval recall, and therefore answer quality. Choose a model suited to your language and domain, use the same model for documents and queries, and evaluate with real questions. Embeddings have limits: they blur exact identifiers such as codes and names, which is why combining them with keyword search in hybrid retrieval improves results. Chunking also matters, because each embedding represents one chunk's meaning.

What is a roadmap for building a RAG pipeline?

Build a simple pipeline end to end first, measure it, then improve the weakest stage. The order matters: ingestion, chunking, embeddings and indexing, retrieval, prompting with citations, then evaluation and iteration.

Also asked as: rag pipeline roadmap

A roadmap:

• Basics: Python, an LLM API, an embedding model, a vector store such as pgvector or Chroma.
• Version 1: load documents, chunk by headings, embed, retrieve top 5, answer with citations.
• Evaluation: 50 real questions with known answers; measure recall and faithfulness.
• Retrieval: add BM25 hybrid search, fusion and a reranker.
• Robustness: refusal threshold, handling of tables and scanned PDFs, updates.
• Production: API, logging, monitoring, cost control and access control.

What is a roadmap for building a RAG chatbot?

Start with a reliable single-turn question-answering RAG, then add conversation: rewriting follow-up questions into standalone queries, keeping short conversation memory, and streaming answers with citations in a chat interface.

Also asked as: rag chatbot roadmap

Steps:

• Build and evaluate single-turn RAG first.
• Add query rewriting so "what about the fees for that?" becomes a full question using chat history.
• Stream answers and show source citations users can click.
• Add guardrails: stay on topic, decline politely, never invent policies.
• Collect feedback with thumbs up or down and review failures weekly.
• Deploy behind authentication if documents are private.

Most chatbot failures are retrieval failures in disguise, so keep measuring retrieval as you add features.

What is a roadmap for agentic RAG?

Agentic RAG lets an agent decide how to retrieve: which sources to search, whether to search again with a refined query, and when it has enough evidence to answer. Learn it after standard RAG is solid.

Also asked as: agentic rag roadmap

A roadmap:

• Master standard RAG and evaluation.
• Expose retrieval as tools, such as search_docs and search_web, with clear descriptions.
• Let the agent plan multi-step retrieval for complex questions.
• Add limits on steps and cost, and require citations in final answers.
• Evaluate on multi-hop questions that need several searches.
• Monitor for loops and irrelevant tool calls.

Use agentic RAG where questions are complex or span sources; for simple document Q&A, a fixed pipeline is cheaper and more predictable.

When was agentic RAG introduced?

Agentic RAG emerged around 2023 to 2024 as language models gained reliable tool calling and agent frameworks matured, letting models control retrieval steps instead of following a fixed pipeline. Surveys describing it as a distinct approach appeared from 2024 onwards.

It builds on research such as ReAct, which interleaves reasoning and actions, and on iterative retrieval methods that refine queries based on intermediate results. In practice, products such as deep research modes in AI assistants use agentic retrieval: they search, read, decide what is missing, search again and then write. The concept will keep evolving as agents become more capable.

What is a RAG assessment?

In project and programme management, a RAG assessment rates status as Red, Amber or Green: green means on track, amber means at risk or minor issues, and red means serious problems needing action. It is unrelated to retrieval-augmented generation.

Also asked as: what is rag assessment

RAG assessments appear in status reports, dashboards and reviews for schedule, budget, scope, risks and quality. Good practice is to define each colour clearly, report amber early with a recovery plan, and explain causes rather than just colours. If you see "RAG assessment" in an AI context, check whether it refers to evaluating a retrieval-augmented generation system, which uses metrics such as retrieval recall and answer faithfulness.

Pranjul Rathour

GenAI Engineer · India

GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU) and BYTEBATTLE (MeetKats).

Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. For campus sessions the inviting institution covers the visit end to end: travel (a First AC train ticket for journeys up to five hours, a flight for anything longer), accommodation, meals, and pickup and drop; the honorarium and workshop fees are negotiable on WhatsApp or on the call, and requests are welcome from institutions that can cover the visit this way. Email pranjulrathour41@gmail.com.

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