The RAG Pipeline Stack: From Ingestion to Deployment

AI agents are not just about choosing an LLM.

Building a production-ready Agentic AI system requires an entire stack — from the user interface to data ingestion, retrieval, prompting, deployment, and evaluation.

Here’s a simple breakdown of the Full Stack of Agentic AI 👇

🔹 Frontend — React, Next.js, Streamlit

🔹 Document Ingestion — OCR, Apache Tika, Microsoft Fabric, LangChain

🔹 Chunking & Preprocessing — preparing data for effective retrieval

🔹 Embeddings — converting information into meaningful vectors

🔹 Vector Databases — Azure AI Search, PostgreSQL, FAISS, Milvus

🔹 Retrieval Layer — finding the right context at the right time

🔹 Prompt Engineering — turning context into useful instructions

🔹 LLMs — Azure, OpenAI, Llama, Mistral

🔹 Infrastructure & Deployment — Docker, Kubernetes, AKS

🔹 Observability & Evaluation — monitoring, tracing, and improving AI systems

The important takeaway?

A great AI application isn't just about the model. It's about how all these layers work together.

💬 Which layer of the Agentic AI stack are you currently learning or working with?

Share it in the comments — I'd love to see what the AI community is focusing on.

#AI #AgenticAI #GenerativeAI #ArtificialIntelligence #MachineLearning #LLM #RAG #AIEngineering #LangChain #OpenAI #Azure #Tech

Building a production-ready RAG-based LLM application requires a full stack — not just a model.

Frontend — React, Next.js, Streamlit

Document Ingestion — OCR (Tesseract), Apache Tika (text/metadata extraction), LangChain document loaders

Chunking & Preprocessing — splitting and structuring data for retrieval

Embeddings — converting text into vector representations

Vector Databases — Azure AI Search, PostgreSQL (pgvector), FAISS, Milvus

Retrieval Layer — surfacing relevant context at query time

Prompt Engineering — shaping retrieved context into effective instructions

LLMs — OpenAI, Meta Llama, Mistral (served via a cloud provider such as Azure OpenAI, AWS Bedrock, or self-hosted)

Infrastructure & Deployment — Docker, Kubernetes, AKS

Observability & Evaluation — monitoring, tracing, and continuous improvement

Note: this is a RAG architecture. A true agentic stack would add orchestration (e.g., LangGraph, CrewAI), tool/function calling, memory, and multi-step planning on top of this foundation.