RAG Starter Stack
Minimum viable stack to ship a production RAG chatbot.
/ quick answer
Ingest documents, embed, retrieve, and generate grounded answers. Minimum viable stack to ship a production RAG chatbot.
- OpenAI / Lovable AI
- pgvector (Postgres)
- LangChain or LlamaIndex
- Next.js or TanStack Start
- Pinecone instead of pgvector
- Vercel AI SDK instead of LangChain
What is the RAG Starter Stack stack for?
Ingest documents, embed, retrieve, and generate grounded answers.
Which tools are in this stack?
OpenAI / Lovable AI, pgvector (Postgres), LangChain or LlamaIndex, Next.js or TanStack Start.
Are there alternatives to this stack?
Yes — Pinecone instead of pgvector, Vercel AI SDK instead of LangChain.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →RAG (Retrieval-Augmented Generation)
Inject external knowledge into an LLM at query time.
- →Vector Database
A database optimized for similarity search over embeddings.
- →Semantic Search
Finding information by meaning rather than exact keyword match.
- →Agentic RAG
RAG where an agent decides what to retrieve, when, and from which source — instead of a single static query.
Related workflows
Turn this into a repeatable process.
- →Build an Internal Knowledge Bot
Ship a Slack bot that answers questions from your company docs.
- →AI Customer Onboarding Flow
Walk every new user through activation with an AI guide.
- →RAG Content Ingestion Pipeline
Convert messy docs into searchable, cited knowledge chunks for AI systems.
- →Build a Tier-1 Customer Support Agent
An agent that handles common tickets end-to-end and hands off the rest.
Related tool stacks
The tools that run it in production.
- →LLM Context Management Stack
A technology stack for effectively managing and optimizing the context provided to large language models, ensuring efficient, relevant, and cost-effective operations.
- →AI Voice Agent Development Stack
This stack outlines essential technologies and tools for building and deploying AI voice agents, encompassing speech processing, natural language understanding, and conversational AI frameworks. It provides a foundation for creating intelligent voice interfaces.
- →AI Voice Assistant Stack
This stack outlines the core technologies for building personal or enterprise AI voice assistants, integrating components for speech recognition, natural language processing, and task execution. It supports intelligent, conversational interfaces for various applications.
- →Low-Cost RAG Stack
This stack combines open-source and cost-efficient components to build a Retrieval-Augmented Generation (RAG) system with minimized operational expenses.
Related prompts
Reusable prompts for this job.
- →Grounded Answer Prompt
Force the model to answer only from provided sources, with citations.
- →RAG Answer With Strict Citations
Force the LLM to answer only from provided chunks and cite them by ID.
Related use cases
How people apply it, and what came out.
- →SaaS Co. Cuts Support Tickets 40% With Internal Bot
A 50-person SaaS deployed a RAG bot over their help center and internal wiki.
- →E-commerce Brand Automates 70% of Support Tickets
A DTC brand deployed a RAG support agent over policies, FAQs, and order data.
- →Support Team Replaces Wiki Sprawl With a Knowledge Graph
A support org connected policies, playbooks, tickets, and RAG answers into one system.
- →SaaS Support Team Automates Tier-1, Focuses on Retention
Support org deflects 55% of tickets and re-invests the time into proactive retention.
Comparisons & alternatives
Pick between the options.
- →RAG vs Fine-Tuning
When to retrieve, when to retrain.
- →Chatbot vs AI Agent
Conversational interface vs autonomous executor.
- →Vector Database vs Knowledge Graph
Similarity retrieval versus explicit relationship mapping.
- →Supabase vs Firebase
Open-source Postgres backend vs Google's managed app platform.