# Perplexity — Basic RAG Implementation: A Step-by-Step Tutorial

- Company: Perplexity (perplexity.ai)
- Announced: 2026-10-28
- Category: not stated
- Coverage: not counted
- Announcement: no
- Group: routine
- Source: https://www.perplexity.ai/hub/blog/rag-implementation
- Record: https://forck.live/items/28801-basic-rag-implementation-a-step-by-step-tutorial
- Subject: Perplexity
- Models affected: pplx-embed-v1-0.6b, GPT-5.6-Luna

Perplexity's tutorial walks beginners through building a basic retrieval-augmented generation system in Python, using ChromaDB and two Perplexity models for embedding and answer generation. The guide argues that while production RAG is complex, a simple architecture helps newcomers understand how the technique works in practice. It breaks the system into three parts: an orchestrator, an information source, and an LLM, and covers indexing and embedding, orchestration, and retrieval and generation. The tutorial builds a command-line application that answers questions about a single document, using pplx-embed-v1-0.6b to embed the document before uploading it to ChromaDB and GPT-5.6-Luna, accessed through Perplexity's API, to process retrieved information. Perplexity says a paid account is not required, though there will be a small credit cost, and it recommends keeping the API key private. The guide also lists best practices: a clean ingestion pipeline, hybrid search, evaluation across accuracy, relevance and latency, and secure handling of private information.

## Evidence

Verbatim from https://www.perplexity.ai/hub/blog/rag-implementation:

> RAG is an essential component of modern LLM-based infrastructure. By allowing AI models to access external data, it helps address issues like hallucinations and incomplete training data. It also means companies can use internal knowledge bases that contain sensitive or proprietary information.
> 
> RAG implementation can be very complex. However, a basic RAG system is surprisingly easy to build. For beginners, creating a simple architecture can illuminate how RAG works in the real world and provide a jumping board for further experimentation.
> 
> In this article, we’ll map out a basic RAG implementation, cover best practices, and then build a working system using Python, ChromaDB, and two Perplexity models for embedding and answer generation.
> 
> Basic RAG implementation process: A high-level overview
> 
> A basic RAG system has three main components: an orchestrator, an information source, and an LLM. In a typical workflow, a user enters a prompt, and the orchestrator modifies it to search any connected information sources. The retrieved information is then added to the original input context and sent to the LLM so a reply can be generated.

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