Build a Retrieval-Augmented Generation Pipeline
Steps to build a RAG pipeline using OpenAI APIs for grounded answers.
- Structure source content.
- Create a vector store using OpenAI File Search API.
- Retrieve relevant context.
- Generate grounded answers using OpenAI Responses API.
- Evaluate answer quality using OpenAI Evals API.
- Tune retrieval behavior.
- Use evals to understand system accuracy and usefulness.
- Source content (documents, text)
- OpenAI API access
- A RAG system that generates grounded answers
- Evaluation metrics for answer quality
This workflow outlines the process of building a retrieval-augmented generation (RAG) pipeline. It involves structuring content, creating a vector store, retrieving context, generating answers, and evaluating their quality using OpenAI's File Search, Responses, and Evals APIs.
This process ensures that AI-generated answers are based on specific, trusted content, improving accuracy and relevance for applications like knowledge assistants and support workflows.
Not suitable for applications that do not require grounding in specific external content.
- Builder Bootcamp: RAG - Event ↗OpenAI Academy · OpenAI Academy · Sep 5, 2026 · paraphrased
First seen on Subvolts Sep 5, 2026. Updated Sep 11, 2026. Spotted a problem? Tell us.
- Builder Bootcamp: RAG - EventOpenAI · OpenAI Academy · Sep 5, 2026
The Playbook: reusable how-tos, prompts, configs and tips pulled out of the news.
- ToolOpenAI Evals API for RAG qualityAPI for evaluating answer quality in RAG systems. · 30 min
- Anti-patternRelying solely on retrieval-augmented generation for finance AIAvoid relying only on RAG for financial AI; use reasoning models to actively analyze and reflect on data. · 5 min
- HeuristicDefine task completion clearly to avoid premature stoppingExplicitly define what 'done' means for a task to ensure AI models complete the full scope. · 10 min
- ToolPlaywright for AI-driven frontend developmentA tool enabling AI models to inspect, validate, and refine frontend implementations autonomously. · 5 min
- HeuristicEnhance AI workflows with clear instructions and focused toolsImprove AI efficiency by providing clear instructions, tools, and stopping conditions. · 10 min
- Anti-patternBuilding agent infrastructure from scratchAvoid piecing together agent infrastructure; use standardized SDKs and native sandbox support. · 5 min
Questions people ask
- Which AI tools does this work with?
- The sources used it with ChatGPT.
- Where did this come from?
- OpenAI Academy (OpenAI Academy). Subvolts collected, normalized and credited it; the link above opens the original.