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Build a Low-Cost RAG App with Claude and GPT

Why build RAG on Claude and GPT?

Retrieval-augmented generation (RAG) is the fastest way to make an AI app answer from your own data instead of guessing. The pattern is simple: store documents in a searchable index, retrieve the most relevant chunks at query time, and send those chunks to a model for a grounded answer. If you want high-quality outputs without locking into one vendor, building a RAG layer that can talk to both Claude and GPT is a strong choice.

That is where 59API is useful. It gives you pay-as-you-go access to Claude models like Opus, Sonnet, Haiku, and Fable, plus GPT models, through a single API base URL: https://api.59api.com. It is fully compatible with the OpenAI SDK, so the same app can work with Claude-backed or GPT-backed models with minimal code changes. For teams watching inference costs, that matters a lot. 59API is positioned as one of the cheapest relays, uses native official-quality models, and even offers a referral rebate.

The architecture

A production-ready RAG pipeline has five pieces:

• Ingest documents from PDFs, docs, or web pages.
• Chunk them into small passages with overlap.
• Embed each chunk into vectors.
• Retrieve the top matches for a user question.
• Generate the final answer with Claude or GPT.

You can use the same retrieval layer for both model families. The main difference is the generation step: Claude is often strong at long-context synthesis, while GPT models are convenient for general instruction following and tool integrations. With 59API, you can test both without changing providers.

Step 1: Set up the client

Install the OpenAI SDK and point it to 59API. Because the relay is compatible with the OpenAI-style interface, the setup is straightforward.

Python example:

from openai import OpenAI

client = OpenAI(
api_key="YOUR_59API_KEY",
base_url="https://api.59api.com/v1"
)

For a first pass, choose a fast model like Haiku or a small GPT model for cheap experimentation. Once your retrieval quality improves, switch to a stronger Claude Sonnet/Opus or GPT model for the final answer generation.

Step 2: Chunk and embed your data

Good chunking is the difference between a useful RAG app and a noisy one. Keep chunks around 300 to 800 tokens, add a small overlap, and preserve metadata such as source URL, title, and section header. That metadata is helpful when you show citations or debug bad retrieval.

A simple ingestion flow looks like this:

1. Load text from files.
2. Split into overlapping chunks.
3. Generate embeddings.
4. Store vectors in a database such as pgvector, Pinecone, or FAISS.

If you are using Python, your chunking code can be as simple as:

def split_text(text, chunk_size=600, overlap=100):
chunks = []
start = 0
while start < len(text):
end = min(start + chunk_size, len(text))
chunks.append(text[start:end])
start = end - overlap
return chunks

For embeddings, you can use whatever embedding model your stack already supports. The key point is that the model choice for generation and retrieval can be independent. That lets you optimize cost and quality separately.

Step 3: Retrieve the best context

When the user asks a question, embed the query and search your vector index for the closest chunks. Return only the top 3 to 6 passages unless the question truly needs more. Too much context can dilute the answer and increase token cost.

Example prompt assembly:

context = "\n\n".join([chunk["text"] for chunk in top_chunks])
prompt = f"""Answer the question using only the context below.

Context:
{context}

Question: {user_question}"""

This is the point where Claude often shines, especially when you want a careful synthesis across multiple documents. GPT models are equally useful when your app needs tool calling, structured outputs, or a style that is easy to control.

Step 4: Generate with Claude or GPT

Because 59API works with the OpenAI SDK, your generation call stays compact. The only thing you change is the model name. That makes A/B testing easy.

Claude example:

response = client.chat.completions.create(
model="claude-sonnet",
messages=[
{"role": "system", "content": "You answer using only provided context."},
{"role": "user", "content": prompt}
]
)

GPT example:

response = client.chat.completions.create(
model="gpt-4o-mini",
messages=[
{"role": "system", "content": "You answer using only provided context."},
{"role": "user", "content": prompt}
]
)

In both cases, you can stream the answer back to the UI, add citations from your metadata, and log the retrieved chunk IDs for later analysis.

Practical tips for keeping costs low

• Cache embeddings so repeated documents are not reprocessed.
• Use a smaller model for retrieval QA and reserve premium models for harder questions.
• Trim context aggressively before sending it to the model.
• Measure answer quality against a small gold set of questions.
• Compare Claude and GPT side by side on your real documents, not just benchmarks.

If you are building an internal knowledge assistant, support bot, or research tool, 59API is a smart place to start because it reduces the friction of testing both model families while keeping spend predictable. The official-quality model access and low relay pricing make experimentation much cheaper than provisioning separate direct accounts.

Wrap-up

The best RAG apps are not the ones with the most complex stack; they are the ones that reliably retrieve the right context and answer clearly. By combining a solid vector index with Claude and GPT through 59API, you get flexibility, low cost, and a clean path to production. If you want to prototype quickly and keep your token bill under control, sign up for 59API and build your first retrieval app today.

Pricing · Docs