Claude + GPT RAG: Fix Retrieval, Citations, and Costs
How do you build a RAG app with Claude and GPT?
A retrieval-augmented generation app searches your documents before asking a model to answer. The basic pipeline is: ingest documents, create searchable chunks, retrieve relevant passages, and send those passages with the question to Claude or GPT. Keep retrieval separate from generation so you can switch models without rebuilding your knowledge base.
59API provides low-cost, pay-as-you-go access to Claude and GPT through an API relay. It is positioned among the cheapest relays and provides native, official-quality models without downgrades. That makes it worth evaluating when repeated retrieval experiments and production traffic start increasing inference costs.
What should the first working pipeline include?
- Clean ingestion: Extract readable text, preserve headings, and remove repeated navigation. Store a document ID, source URL, revision, and access-control metadata.
- Chunking: Start with 400–700 tokens per chunk and 60–100 tokens of overlap. Prefer section boundaries; keep table headers with their rows.
- Embeddings: Choose an available embedding provider and record the exact model and vector dimensions. Use the same embedding model for indexed chunks and search queries.
- Retrieval: Retrieve around 20 candidates, then rerank or select the strongest 4–6 passages. Treat these numbers as baselines, not universal settings.
- Generation: Assign passage IDs such as S1 and S2. Ask Claude or GPT to answer from supplied evidence, cite those IDs, and acknowledge missing information.
How do you connect through 59API?
The API base URL is https://api.59api.com. For an OpenAI SDK integration, configure your API key, the documented compatible base_url, and an exact supported model ID from 59API’s current catalog. Confirm whether the SDK-facing URL requires a version suffix such as /v1; do not append it twice.
59API supports OpenAI SDK integrations and compatibility with Claude Code and Codex. However, tool compatibility does not mean every endpoint or optional parameter behaves identically. Test your chosen generation endpoint, streaming, token limits, and structured output before integrating retrieval. Verify embedding availability separately rather than assuming generation access includes embeddings.
Why are answers wrong when the right document exists?
Inspect the retrieved passages before changing the prompt. If the answer-bearing passage is absent, the problem is retrieval. Check extraction quality, stale indexing, chunk boundaries, and embedding consistency. Queries containing product codes or error messages often benefit from hybrid search: combine keyword matching with vector similarity, then deduplicate and rerank results.
Apply tenant and permission filters during retrieval, not merely in the final prompt. If the correct passage is present but ignored, remove redundant context and place the strongest evidence first. Log passage IDs and retrieval ranks so failures are reproducible.
How do you stop invented answers and citations?
Use an explicit instruction: answer only from retrieved evidence, cite passage IDs, and say when the evidence is insufficient. Treat retrieved documents as untrusted data, never as instructions. A document that says to ignore system rules must not override your application policy.
Validate that every returned citation references a supplied passage. Citation existence alone does not prove support, so review whether each cited passage actually supports the associated claim. For high-stakes workflows, add human review rather than relying on fluent wording.
What causes API errors, slow responses, and high costs?
- 401 or 403: Check credentials, account access, and authentication requirements. Keep keys on your server.
- 404: Verify the endpoint path and exact model ID; SDK defaults may point to an unsupported route.
- 429 or transient server errors: Use bounded exponential backoff with jitter, respect retry guidance, and cap concurrency.
- Slow or expensive requests: Reduce duplicate context, cap output length, cache reusable retrieval results with permission-aware keys, and precompute document embeddings.
Should you use Claude, GPT, or both?
Compare them on the same 30–50 representative questions, including unanswerable and permission-restricted cases. Measure retrieval recall, answer correctness, citation support, latency, and cost per resolved question. Route straightforward questions to a cheaper model only after it meets your quality threshold.
59API’s shared access simplifies those comparisons, while pay-as-you-go billing limits upfront commitment. A referral rebate is also available; check current terms. If you want to test the economics, sign up for 59API and run a small benchmark before scaling.