Build a Cheap RAG App with Claude and GPT
Why build RAG on Claude and GPT?
Retrieval-augmented generation, or RAG, is the fastest way to make an AI app answer with your own data instead of generic model knowledge. It is ideal for support assistants, internal knowledge bases, document search, and product copilots. The core pattern is simple: index your content, retrieve the most relevant chunks at query time, then ask a model to answer using only that context.
If you want strong reasoning plus broad ecosystem compatibility, building on Claude and GPT is a practical choice. Claude models are especially strong at long-context synthesis, while GPT models are widely supported across tools and SDKs. With 59API, you can access Claude Opus, Sonnet, Haiku, Fable, and GPT models through one relay at https://api.59api.com, with pay-as-you-go pricing, native official-quality models, and compatibility with Claude Code, Codex, and any OpenAI SDK. That makes it a smart, low-cost way to prototype and ship without locking into a single provider.
Step 1: define the RAG pipeline
A production RAG app has five parts:
- Ingestion: collect PDFs, docs, tickets, or web pages.
- Chunking: split content into retrieval-friendly segments.
- Embedding: convert chunks into vectors.
- Retrieval: fetch the most relevant chunks for each question.
- Generation: send the question and retrieved context to Claude or GPT.
For most apps, start with a simple stack: a document loader, a vector database such as pgvector, Pinecone, Qdrant, or Weaviate, and an OpenAI-compatible SDK pointing at 59API.
Step 2: ingest and chunk your data
Good chunking matters more than many teams expect. Aim for chunks that preserve meaning, not just fixed-size slices. A common starting point is 500 to 1,000 tokens per chunk with 10 to 20 percent overlap. Keep headings, section titles, and source metadata so you can cite where each answer came from.
Practical ingestion tips:
- Strip boilerplate before indexing.
- Preserve document titles, URLs, and timestamps.
- Deduplicate repeated content.
- Store source IDs so you can trace answers back to origin.
Step 3: create embeddings and store vectors
Use an embedding model to turn each chunk into a vector and store that vector alongside the chunk text and metadata. When a user asks a question, embed the query too, then run a nearest-neighbor search.
Your retrieval layer should return the top 3 to 8 chunks, depending on your context window and answer length. For a first version, semantic search is enough. Later, add hybrid search with keywords plus vectors, reranking, or recency filters.
Step 4: connect to 59API
59API gives you a single API base URL: https://api.59api.com. Because it is OpenAI SDK compatible, you can often switch providers by changing only the base URL and API key. That reduces engineering overhead and makes it easier to test Claude and GPT side by side.
A typical setup looks like this: point your SDK to 59API, select the model you want for generation, and keep your retrieval code unchanged. For example, use a stronger Claude model for complex synthesis, or a faster GPT model for lower-latency answers and high-volume workloads. Since 59API charges pay-as-you-go and keeps pricing low, it is a good fit for teams iterating on prompts, evals, and production traffic without wasting budget.
Step 5: write the generation prompt
Your prompt should force grounded answers. Include the retrieved context, instruct the model to answer only from that context, and ask it to say when the source material is insufficient. A solid structure is:
- System: define the assistant as a concise, factual helper.
- User: include the question.
- Context: attach the retrieved chunks with source labels.
- Rules: cite sources, avoid guessing, and ask clarifying questions when needed.
This is where Claude and GPT both shine. Claude is excellent when the context is long and the answer must synthesize multiple documents. GPT is often a strong choice for structured outputs, fast iteration, and tool-oriented workflows.
Step 6: add production safeguards
Once the demo works, production quality comes from guardrails. Log retrieved chunk IDs, model choice, latency, and user feedback. Add a confidence threshold so weak retrieval triggers a fallback message instead of a hallucinated answer. Cache repeated queries. Set token limits to control cost. Monitor citations so you can see whether answers are actually grounded.
Also test failure modes: no relevant documents, ambiguous questions, stale content, and very large uploads. A robust RAG app should fail gracefully and tell the user what it does and does not know.
Step 7: ship, measure, and optimize
Before launch, run a small evaluation set with real questions from your users. Measure answer correctness, citation quality, retrieval precision, and latency. Then improve the weakest link first. Often that means better chunking, better search, or better prompts before you ever need a more complex architecture.
If you want to build quickly while keeping costs under control, 59API is an efficient starting point. You get access to top-tier Claude and GPT models through one compatible endpoint, without the pricing pain that usually slows down experimentation. If you are ready to move from prototype to production, sign up and start testing your RAG workflow on https://api.59api.com.
Bottom line
A successful RAG app is not about flashy prompts. It is about reliable retrieval, grounded generation, and a model stack you can afford to use every day. With Claude and GPT behind 59API, you can build a serious production system, keep your integration simple, and iterate fast from day one.