Langchain

A dashboard showing a RAG system answering questions

Build an End-to-End RAG System for Your Portfolio

Learn to build a production-style RAG pipeline that demonstrates end-to-end systems engineering, from data ingestion to API serving.

A conceptual diagram of a RAG pipeline with document ingestion, vector store, retriever, re-ranker, and LLM generator stages.

Building a Production-Grade RAG Pipeline: A Hands-On Portfolio Project That Signals Real Systems Skill

Move beyond the generic FAISS + LangChain + OpenAI tutorial. This guide walks you through building a multi-stage RAG pipeline with hybrid retrieval, cross-encoder re-ranking, and automated evaluation — the kind of project that signals real systems skill on a CV.

Abstract visualization of code transforming into an agent orchestration layer, with nodes and data flow representing autonomous agent behavior.

Code as Agent Harness

Code as Agent Harness reframes the relationship between application code and AI agents. Instead of writing prompts, engineers design harnesses — structured code layers that define tools, guardrails, and orchestration logic for autonomous systems.

Building High‑Performance RAG Systems with Pinecone Vector Indexing and LangChain Orchestration

Table of Contents Introduction Understanding Retrieval‑Augmented Generation (RAG) 2.1. What Is RAG? 2.2. Why RAG Matters Core Components: Vector Stores & Orchestration 3.1. Pinecone Vector Indexing 3.2. LangChain Orchestration Setting Up the Development Environment Data Ingestion & Indexing with Pinecone 5.1. Preparing Your Corpus 5.2. Generating Embeddings 5.3. Creating & Populating a Pinecone Index Designing Prompt Templates & Chains in LangChain Building a High‑Performance Retrieval Pipeline Scaling Strategies for Production‑Ready RAG Monitoring, Observability & Cost Management Real‑World Use Cases Performance Benchmarks & Optimization Tips Security, Privacy & Data Governance Conclusion Resources Introduction Retrieval‑Augmented Generation (RAG) has become the de‑facto pattern for building AI systems that need up‑to‑date, domain‑specific knowledge without retraining massive language models. The core idea is simple: retrieve relevant context from a knowledge base, then generate an answer using a language model that conditions on that context. ...

Building Autonomous Agentic RAG Pipelines Using LangChain and Vector Database Sharding Strategies

Introduction Retrieval‑Augmented Generation (RAG) has reshaped the way developers build knowledge‑aware applications. By coupling large language models (LLMs) with a vector store that can quickly surface the most relevant chunks of text, RAG pipelines enable: Up‑to‑date answers that reflect proprietary or frequently changing data. Domain‑specific expertise without costly fine‑tuning. Scalable conversational agents that can reason over millions of documents. When you add autonomous agents—LLM‑driven programs that can decide which tool to call, when to retrieve, and how to iterate on a response—the possibilities expand dramatically. However, real‑world workloads quickly outgrow a single monolithic vector collection. Latency spikes, storage costs balloon, and multi‑tenant requirements become impossible to satisfy. ...