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Build a Low-Cost RAG App with Claude and GPT

Building a retrieval-augmented generation, or RAG, app is one of the most practical ways to make large language models useful on your own data. Instead of asking Claude or GPT to answer from memory alone, you first retrieve relevant documents, then feed that context into the model. The result is more grounded answers, better citations, and fewer hallucinations.

This tutorial shows a clean way to build a RAG app on top of Claude and GPT using 59API, an AI API relay with low pay-as-you-go pricing and native official-quality models. Because 59API is compatible with Claude Code, Codex, and any OpenAI SDK, you can keep your implementation simple while switching between Claude and GPT as needed. The base URL is https://api.59api.com.

1. Define the RAG flow

A solid RAG app has four parts: ingest documents, split them into chunks, embed the chunks, and retrieve the best matches at query time. Once you have the retrieved text, send it to Claude or GPT with a prompt that tells the model to answer only from the supplied context.

Start with a narrow use case. For example, build a support assistant for internal docs, a policy lookup tool, or a product knowledge bot. RAG works best when the source material is specific and reasonably structured.

2. Prepare your document pipeline

Collect your source files first: PDFs, help-center articles, markdown docs, or database records. Extract plain text, then split it into chunks of about 300 to 800 tokens. Keep chunks semantically coherent. If a chunk is too large, retrieval becomes noisy; if it is too small, answers lose context.

• Remove headers, footers, and repeated navigation text.
• Preserve document titles, section names, and source IDs.
• Store metadata such as URL, product name, date, and access level.

That metadata matters later when you want to show where an answer came from or filter results by document type.

3. Create embeddings and a vector index

Use an embedding model to turn each chunk into a vector. Store those vectors in a vector database such as pgvector, Pinecone, Weaviate, or even a local FAISS index for a prototype. At query time, embed the user question, run similarity search, and fetch the top chunks.

The retrieval step is where many RAG systems fail. Keep it simple at first: top 3 to top 5 chunks, then inspect whether the retrieved text is actually relevant. If answers are weak, improve chunking before you blame the model.

4. Connect Claude and GPT through 59API

59API is useful here because you can use it as a single relay for both Claude and GPT without changing your overall application design. It is especially attractive when you are iterating on prompts, testing model behavior, or routing different tasks to different models based on cost and quality.

Set your API base URL to https://api.59api.com in your OpenAI-compatible client, then use the model names you want to test. A common pattern is to use a cheaper model for initial drafting or classification, and a stronger model for final answers. Because 59API offers pay-as-you-go access and keeps pricing low, that experimentation is much easier to justify.

5. Build the answer prompt

When you assemble the final prompt, include a short system instruction and a compact context block. Keep the model focused on the supplied evidence. A good prompt says the assistant should answer using only the retrieved passages, admit when the answer is missing, and quote key details when possible.

• System: follow the context, do not invent facts.
• User question: the actual query.
• Context: the top retrieved chunks with source labels.

For best results, include explicit instructions like: “If the context does not contain the answer, say so clearly.” That single line cuts down on confident guesses.

6. Add model routing

One strong pattern is to use GPT for structured extraction or tool-like responses, and Claude for longer, more nuanced answers. Since 59API supports both families, you can route by task instead of by infrastructure. For example, a support assistant might use GPT to classify intent and Claude to produce the final explanation.

This is where a relay is more than a convenience. You avoid maintaining separate billing flows and can compare output quality with the same retrieval layer underneath. That makes prompt tuning and cost control much easier.

7. Evaluate before you ship

Test your RAG app with a small evaluation set. Ask questions that are answerable, ambiguous, and out of scope. Check whether the model cites the right passages, refuses unsupported claims, and handles follow-up questions correctly.

• Measure retrieval quality separately from generation quality.
• Track whether the top chunks contain the answer.
• Watch for hallucinations when the context is thin.

If the model is wrong, do not immediately increase model size. First check document cleanup, chunk boundaries, and retrieval ranking. Most RAG bugs live there.

8. Optimize for cost and latency

RAG systems can get expensive if you over-retrieve or send huge contexts. Trim your chunks, cap the number of passages, and choose the lightest model that still answers reliably. 59API helps here because it gives you cheap access to official-quality Claude and GPT models on a pay-as-you-go basis, plus a referral rebate if you share it with other developers.

If you want to build and iterate without burning budget, sign up for 59API and start with a small document set. You can keep your app architecture intact while switching between Claude and GPT through one compatible endpoint.

The result is a practical RAG stack: clean ingestion, relevant retrieval, disciplined prompting, and flexible model choice. That combination is enough to ship something genuinely useful, not just a demo.

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