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Build a RAG App with Claude, GPT and 59API

Build a RAG App with Claude, GPT and 59API

Retrieval-augmented generation, usually called RAG, is one of the most practical ways to build useful AI apps. Instead of asking a model to answer from memory, your app first retrieves relevant information from your own documents, then sends that context to a model such as Claude or GPT. The result is an assistant that can answer questions about product docs, policies, tickets, research notes, internal wikis, or codebases with far better accuracy.

This beginner-friendly guide walks through the real components of a RAG app and explains how to build one on top of Claude and GPT models using 59API, an AI API relay that provides cheap, pay-as-you-go access to Claude Opus, Sonnet, Haiku, Fable, and GPT models. Because 59API is compatible with Claude Code, Codex, and any OpenAI SDK, you can usually switch your API base URL to https://api.59api.com and keep your existing developer workflow.

What a RAG App Actually Does

A basic RAG pipeline has five steps. First, you collect documents. Second, you split them into smaller chunks. Third, you turn those chunks into embeddings, which are numerical representations of meaning. Fourth, you store those embeddings in a vector database. Finally, when a user asks a question, you retrieve the most relevant chunks and pass them to Claude or GPT with instructions to answer using only that context.

This approach is valuable because even strong models can hallucinate when they lack the right information. RAG gives the model fresh, domain-specific facts without fine-tuning. It is also easier to update: when your documentation changes, you update your index rather than retraining a model.

Recommended Architecture

• Frontend: A chat UI built with React, Next.js, Vue, or a simple server-rendered page.
• Backend: A small API server in Node.js, Python, Go, or another language you prefer.
• Document storage: Files from PDFs, Markdown, Notion exports, web pages, or database records.
• Chunking layer: Code that splits long documents into chunks of roughly 300 to 800 tokens.
• Embedding model: Used to convert chunks and user questions into vectors.
• Vector database: Pinecone, Qdrant, Weaviate, Chroma, pgvector, or another similarity search tool.
• Generation model: Claude for careful reasoning and long-context answers, or GPT for fast, broad-purpose responses.

Step 1: Prepare and Chunk Your Documents

Start with a small, clean dataset. For example, use your top 20 help center articles or a folder of Markdown files. Extract plain text, remove repeated navigation text, and preserve useful metadata such as title, URL, document type, and last updated date.

Then split the text into chunks. Avoid making chunks too small, because the model may miss surrounding context. Avoid making them too large, because retrieval becomes less precise. A good starting point is 500 tokens per chunk with a 50-token overlap. Store each chunk with metadata so you can cite sources later.

Step 2: Create Embeddings and Store Vectors

For every chunk, call an embedding model and store the resulting vector in your vector database. Each record should include the vector, chunk text, document title, source URL, and any tags that help filtering. If your app supports multiple customers or teams, include a tenant ID and always filter by it during search.

When a user asks a question, create an embedding for that question too. Search your vector database for the top matches, often top 5 to top 10. In many apps, you will get better results by adding filters, such as searching only published docs or only documents from a specific workspace.

Step 3: Send Context to Claude or GPT

Once you have the best chunks, build a prompt that clearly separates instructions, retrieved context, and the user question. A strong system instruction might say: Answer using the provided context. If the answer is not in the context, say you do not know. Cite the source titles when helpful.

Then call a Claude or GPT model through 59API. If you already use an OpenAI-compatible SDK, configure the API base URL as https://api.59api.com and provide your 59API key. This makes it straightforward to test different models without rewriting your app. You might use Claude Sonnet for balanced reasoning, Claude Haiku for lower-cost responses, or a GPT model for general chat and tool-heavy workflows.

Step 4: Add Practical Quality Controls

A production RAG app needs more than retrieval and generation. Add guardrails that improve trust and reduce cost. Limit context length so you do not send unnecessary tokens. Deduplicate similar chunks before prompting. Ask the model to include citations. Log user questions, retrieved document IDs, model choice, latency, and token usage so you can debug bad answers.

You should also evaluate your app with a simple test set. Write 30 to 50 real questions and expected answers. Run them whenever you change chunking, retrieval settings, prompts, or models. This prevents silent regressions and helps you choose the best model for each task.

Why Use 59API for a RAG Project

RAG apps can make many model calls: embeddings during indexing, retrieval-time query embeddings, chat completions, reranking, summarization, and evaluation. Cost matters, especially when you are experimenting. 59API is designed as a low-cost AI API relay with pay-as-you-go access, official-quality native models with no downgrade, and compatibility with Claude Code, Codex, and OpenAI SDK-based projects.

That combination is useful for developers because you can start small, compare Claude and GPT behavior, and optimize for quality, latency, and price without committing to a complicated migration. 59API also offers a referral rebate, which can further reduce costs for teams sharing the platform with other builders. If you are starting a RAG prototype, it is worth signing up and testing your first workflow through 59API before scaling usage.

Final Tips for Beginners

• Start with fewer documents: Debug retrieval quality before indexing everything.
• Keep chunks readable: The model should receive complete ideas, not random fragments.
• Use citations: They make answers easier to verify.
• Measure cost per answer: Track tokens, model choice, and retrieval size.
• Test multiple models: Claude and GPT may perform differently depending on your domain.

Building a RAG app is not just about connecting a chatbot to documents. It is about designing a reliable flow that retrieves the right evidence, gives the model clear instructions, and makes answers easy to verify. With a vector database, careful chunking, and low-cost access to Claude and GPT through 59API, you can build a useful knowledge assistant without a large infrastructure budget.

Pricing · Docs