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RAG Troubleshooting: Claude and GPT via 59API

Retrieval-augmented generation (RAG) gives Claude or GPT access to your private documents without retraining the model. A typical pipeline loads files, splits them into passages, creates embeddings, retrieves the most relevant passages, and sends those passages with the user question to a language model. The architecture is straightforward, but most failures come from retrieval quality, context handling, or API configuration.

How should I structure a RAG pipeline?

Separate the system into four stages: ingestion, indexing, retrieval, and generation. During ingestion, extract text while preserving useful metadata such as document title, page number, URL, author, and access permissions. Split content into chunks of roughly 400 to 800 tokens with a modest overlap. Split at headings or paragraph boundaries where possible; arbitrary character cuts often remove the context needed to answer a question.

Create embeddings with your existing embedding provider or a local embedding model, then store vectors alongside the original text and metadata. At query time, embed the question, retrieve the top 5 to 10 candidates, optionally rerank them, and pass only the strongest evidence to Claude or GPT through 59API. Keeping retrieval independent from generation makes it easier to switch models and control costs.

Why does the model give an incorrect answer?

First, inspect the retrieved passages before changing the prompt. If the correct passage is missing, the problem is indexing or search, not Claude or GPT. Check for failed PDF extraction, duplicated chunks, overly large chunks, missing metadata filters, and queries that use different terminology from the source material.

If the right evidence is present but ignored, improve the generation instructions. Tell the model to answer only from the supplied context, state when the evidence is insufficient, and cite the document title or page metadata. A useful prompt also separates the user question from retrieved text so document content cannot accidentally override your system instructions.

Which model should handle generation?

Use a fast, lower-cost model for routine lookups and a stronger model for synthesis, ambiguous questions, or multi-document comparisons. For example, Claude Haiku 4.5 can handle concise support answers, while Claude Sonnet 5.5 is a better choice when the response requires careful reasoning and source comparison. GPT-6.1 Sol is another option when your application already uses an OpenAI-compatible client and needs a general-purpose generation model.

59API provides pay-as-you-go access through the base URL https://api.59api.com, so you can test several models without committing to separate large platform contracts. It exposes native official-quality models rather than downgraded substitutes, and works with OpenAI SDK integrations as well as Claude Code, Codex, and Gemini CLI. This makes model routing practical: reserve premium calls for difficult requests and use an economical model for predictable retrieval answers.

What causes context-window or token errors?

Retrieving more text is not always better. Start with a small top-k value, remove near-duplicate passages, and set a maximum character or token budget before constructing the request. Include the question, instructions, and evidence in your budget. If answers degrade as documents grow, add reranking or use a two-step process: retrieve broadly, then summarize the best passages before final generation.

Why are citations unreliable?

Never ask the model to invent citations after the answer is written. Attach stable source identifiers to every chunk and require each factual claim to reference one of those identifiers. In your application, validate that cited identifiers actually appeared in the retrieved set. Display the source title, page, or URL from your database rather than trusting free-form citation text.

How do I control RAG costs?

Cache document embeddings, cache repeated questions, cap retrieved context, and stream responses where appropriate. Log input tokens, output tokens, latency, retrieved chunk IDs, and user feedback. This reveals whether cost is caused by excessive context or by selecting a premium model unnecessarily. Because 59API is a low-cost relay with pay-as-you-go billing and a referral rebate, it is a practical place to benchmark Claude and GPT choices before scaling. Sign up for 59API, configure your client with its API base URL, and begin with a small evaluation set of real questions.

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