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Satish Kumar
Satish Kumar

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Retrieval-Augmented Generation (RAG) Powered Conversational Chatbot Solution: Concepts and Tech Stack You Need to Build It

#architecture #llm #ai #rag

LLMs are powerful, but they don’t know your data. Retrieval-Augmented Generation (RAG) bridges this gap by combining document retrieval with large language models (LLMs) to produce grounded, context-aware answers.

This post focuses on the concepts behind RAG and the technology stack required to implement it end-to-end. No code yet—just the mental model and architectural components.

🌐 What is RAG?

I am sure all of you know definition of RAG so I would directly dive into technical details.

At its core, RAG has two loops:

  1. Indexing (knowledge preparation)

    • Ingest raw documents → chunk → embed → store in a search index.

  2. Answering (knowledge retrieval)

    • User query → embed → retrieve relevant chunks → combine with query → LLM produces a grounded answer.

RAG ensures the LLM’s answers are accurate, up-to-date, and specific to your domain, while reducing hallucinations.

🏗️ The Tech Stack for a RAG Solution

A production-grade RAG system combines several technologies. Here’s the end-to-end flow and the role of each stack:

1. Document Storage (Raw Knowledge Repository)

  • Role in RAG: Store unprocessed source documents (PDFs, Word, text, HTML, etc.).
  • Examples:
    • Cloud: Azure Blob Storage, AWS S3, Google Cloud Storage
    • On-prem: File servers, databases
  • Why needed? RAG starts with your documents. Storage is the “bookshelf” of your knowledge base.

2. Data Processing / Chunking

  • Role in RAG: Split documents into manageable chunks (e.g., 500–2000 tokens) so they can be embedded and retrieved effectively.
  • Examples:
    • Libraries: LangChain, LlamaIndex, Haystack
    • Custom scripts for splitting by paragraphs, sections, or semantic boundaries
  • Why needed? LLMs can’t handle arbitrarily large docs. Chunking ensures recall and context fit into the LLM’s token window.

3. Embedding Model

  • Role in RAG: Convert text chunks and queries into vector representations (numerical arrays).
  • Examples:
    • Cloud: Azure OpenAI Embeddings (text-embedding-3-large), OpenAI, Cohere, Hugging Face models
  • Why needed? Vectors allow semantic similarity search—finding “meaningful” matches, not just keyword matches.

4. Vector Database / Search Index

  • Role in RAG: Store embeddings + metadata; enable fast vector search (kNN) and hybrid search (vector + keyword).
  • Examples:
    • Cloud-native: Azure Cognitive Search, Pinecone, Weaviate, Milvus, Qdrant
    • Traditional DBs with vector support: PostgreSQL + pgvector, MongoDB Atlas Vector Search
  • Why needed? This is the “librarian” that quickly finds the most relevant passages.

5. Retriever / Orchestrator

  • Role in RAG: Execute retrieval strategy—take the user’s query, embed it, run vector search, and format retrieved chunks for the LLM.
  • Examples:
    • Frameworks: LangChain, LlamaIndex, Semantic Kernel
  • Why needed? Retrieval is more than search—it decides how many chunks, which filters, and how to pass context to the LLM.

6. LLM (Answer Generator)

  • Role in RAG: Use the query + retrieved context + System Prompt + Guardrail to generate a grounded, user-friendly answer.
  • Examples:
    • Cloud: Azure OpenAI GPT (GPT-4o, GPT-4o mini), Anthropic Claude, Google Gemini
    • Open-source: LLaMA 3, Mistral, Falcon (if running locally)
  • Why needed? The LLM is the “writer” that crafts fluent, contextualized answers, but only after being given the right pages.

7. Application Layer (Client + API)

  • Role in RAG: Provide UI and API endpoints for upload, search, and Q&A.
  • Examples:
    • Frontend: React.js, Next.js (file upload, chat UI)
    • Backend: Node.js, Python FastAPI/Flask (to orchestrate workflows and hide secrets)
  • Why needed? This is what end-users interact with—whether it’s a chatbot, a search bar, or an API service.

8. Supporting Services (Optional but critical in production)

  • Authentication & Security: Microsoft Entra ID (Azure AD), OAuth, API gateways
  • Observability: Application Insights, Datadog, Prometheus for logging, metrics, tracing
  • Secrets Management: Azure Key Vault, AWS Secrets Manager
  • Eventing / Pipelines: Event Grid, Kafka, Airflow for auto-ingestion

🔄 RAG Flow with Roles

Here’s how these pieces interact conceptually:

  1. Document arrives → stored in Blob Storage.
  2. Chunking pipeline splits text → each chunk is embedded with an embedding model.
  3. Vector database (Cognitive Search) stores vectors + metadata for retrieval.
  4. User asks a question in the frontend app.
  5. Backend embeds the query → queries vector DB → retrieves top-k chunks.
  6. Retriever passes query + chunks into the LLM with System Prompt + Guardrail → grounded answer generated.
  7. User sees answer + citations in the UI.

🧠 Mental Model

Think of RAG as a library system:

  • Blob Storage = the bookshelf of all books (raw documents)
  • Chunking + Embeddings = indexing the book pages by meaning
  • Vector DB / Search = the librarian who finds the right pages fast
  • Retriever = the assistant who picks which pages to show the author
  • LLM = the author who writes the summary/answer
  • Frontend/API = the reading room where users ask and receive answers

⚡ Key Takeaways

  • RAG augments LLMs with your private, dynamic knowledge.
  • Each component in the stack plays a distinct role (storage, search, reasoning).
  • The solution is modular: you can swap components (different vector DB, different LLM) as needed.
  • Once you understand the flow, you can extend it: add auto-ingestion, filtering, reranking, and observability.

👉 In the next blog, we can move from concepts to a POC app with working code, showing how these stacks fit together in practice to make it a live app.

👉 References: https://learn.microsoft.com/en-us/azure/search/retrieval-augmented-generation-overview?tabs=docs
https://python.langchain.com/docs/tutorials/rag/

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