IntermediateИнженерия

OpenAI RAG & Production Patterns

Official OpenAI production examples: PDF parsing for RAG, Q&A via Search API, prompt caching, rate limit handling, long document summarization, and entity extraction. Real code from the OpenAI team.

2modules

6lessons

150 mintotal time

Developers deploying OpenAI in production systemsaudience

Module 1

RAG: Data Ingestion and Retrieval

Build a RAG pipeline from scratch: parse PDFs with GPT-4o vision, create per-page embeddings, and answer questions with HyDE re-ranking.

Parsing PDFs for RAG: GPT-4o + embeddings

Turn complex PDFs (slide decks, reports) into retrieval-ready content: dual parsing with pdfminer (text) and GPT-4o vision (images), per-page chunking, text-embedding-3-small embeddings, cosine similarity search.

25 min

Q&A via Search API and HyDE Re-ranking

Build RAG on top of an external Search API (NewsAPI): model-generated query variants, article retrieval, HyDE (Hypothetical Document Embeddings) re-ranking, streamed answer with source citations.

25 min

Module 2

Production Optimization

Prompt caching to cut costs and latency, graceful rate limit handling with exponential backoff, and structured extraction from long documents.

Prompt Caching: -80% latency on repeated prompts

OpenAI automatically caches prompts from 1024 tokens onwards. Learn the mechanics of caching tools, images, and system prompts, monitor cache_read_input_tokens, and follow best practices for static-first prompt layout.

25 min

Rate Limits: exponential backoff and batch throughput

Handle 429 RateLimitError without crashes: tenacity and backoff decorators, manual exponential backoff with jitter, model fallback, and proactive delay for high-throughput batch jobs.

25 min

Summarizing long documents with controllable detail

Summarize documents over 10k tokens with a detail parameter from 0 to 1: delimiter-based chunking, parallel per-chunk summarization, and recursive summarization for narrative coherence.

25 min