← Back to all guides

Build a Dual-Model RAG App with Claude and GPT

Build a RAG App That Can Use Claude or GPT

Retrieval-augmented generation, or RAG, lets an AI answer from your company’s documents instead of relying only on its training data. A practical production setup has four stages: ingest source files, split and embed the text, retrieve relevant passages for each question, and ask a language model to generate a grounded answer. Supporting both Claude and GPT gives you useful flexibility: route difficult reasoning tasks to a stronger model, use a faster model for routine questions, and compare answer quality without rebuilding your application.

59API is a useful gateway for this architecture because it provides pay-as-you-go access to native official-quality Claude and GPT models through one low-cost relay. Its API base URL is https://api.59api.com, and it works with OpenAI SDK-compatible clients, Codex, and Claude Code. That means your RAG service can keep one client abstraction while selecting the best available model for each request.

Step 1: Define the Knowledge Boundary

Start with a narrow, maintainable corpus. For example, an internal support assistant might index product manuals, release notes, approved troubleshooting articles, and policy documents. Do not mix unreviewed chat exports or stale drafts into the first version. RAG quality depends heavily on source quality, and bad documents will produce confidently wrong answers even when retrieval is technically correct.

• Store metadata: Save each document’s title, URL, product area, version, access level, and last-updated date.
• Preserve source references: Keep a document ID and chunk ID for every text segment so the application can show citations.
• Plan updates: Re-index changed files on a schedule or from a webhook rather than rebuilding the entire index every time.

Step 2: Chunk and Embed Your Documents

Extract clean text from PDFs, HTML pages, DOCX files, or Markdown. Split it by headings and paragraphs before applying a token-based chunk limit. A good initial target is 400 to 700 tokens per chunk with 50 to 100 tokens of overlap. This is large enough to preserve context but small enough that search returns focused evidence. Attach the document metadata to every chunk and write the vectors plus text to a vector database such as pgvector, Pinecone, Qdrant, or Weaviate.

Use one embedding model consistently for a collection. At query time, embed the user’s question with that same model, retrieve the top 8 to 12 chunks, and then filter by metadata. For example, a question about version 3.2 should not retrieve instructions for version 2.0 if the user selected the newer product release.

Step 3: Add Retrieval, Ranking, and Context Controls

Vector similarity alone is rarely enough for a polished RAG app. Retrieve a broader candidate set, then apply keyword matching or a reranker to prioritize chunks containing exact product names, error codes, or policy terms. Deduplicate near-identical chunks from the same page. Finally, select only the passages that fit your context budget. Passing every retrieved chunk to Claude or GPT increases cost and can distract the model from the best evidence.

• Return citations: Include a source title and URL beside each final answer.
• Set a relevance threshold: If no chunk scores well, say that the knowledge base does not contain a verified answer.
• Protect instructions: Treat retrieved text as reference material, not as system instructions. Documents may contain misleading or malicious prompt-like content.

Step 4: Route Generation Through 59API

Configure your OpenAI-compatible client with the 59API base URL, your 59API key, and the exact model identifier shown in the 59API dashboard. Keep the API key in an environment variable, never in browser code. Your server can expose a simple generateAnswer function that receives the user question, selected source chunks, and a model choice.

A practical routing policy is to send short FAQ-style questions to a lower-latency, economical model such as Claude Haiku or a smaller GPT option, while assigning complex multi-document synthesis to Claude Sonnet, Claude Opus, or a capable GPT model. Claude Fable can also be evaluated for workflows where its available capabilities fit your task. Because 59API offers Claude and GPT access without model downgrades, you can test native model behavior while keeping relay costs predictable. This is especially valuable when RAG traffic grows from a prototype into a customer-facing service.

Step 5: Use a Grounded Prompt and Evaluate It

Your generation prompt should be explicit: answer only from the supplied sources, cite the source IDs used, state when the evidence is insufficient, and do not invent product behavior. Include the user question followed by clearly separated source passages. Ask for a concise answer first, then actionable steps if the question requires troubleshooting.

Before launch, create a test set of 30 to 50 real questions. Measure retrieval recall, citation accuracy, answer correctness, latency, and cost per answer for both Claude and GPT. Log the question, retrieved chunk IDs, model, prompt version, response, and user feedback. This makes failures diagnosable: you can tell whether the issue was missing content, weak retrieval, a poor prompt, or an inappropriate model route.

Launch with Cost and Quality Visibility

Deploy the RAG API behind authentication, enforce document-level permissions before retrieval, and cache repeated answers where appropriate. Track token usage separately for retrieval context and generated output. With 59API’s low-cost, pay-as-you-go model access, you can run controlled Claude-versus-GPT evaluations without committing to a single provider path. When you are ready to test your routing policy in production, sign up for 59API and use its referral rebate to reduce ongoing experimentation costs.