Automated Enterprise Reporting: PDF Generation with Python & OpenAI API
A technical step-by-step tutorial on building an automated PDF reporting pipeline using Python, Pandas, and the OpenAI API for AI-generated summaries.
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Follow this category by RSSA technical step-by-step tutorial on building an automated PDF reporting pipeline using Python, Pandas, and the OpenAI API for AI-generated summaries.
A technical step-by-step tutorial on deploying and scaling serverless microservices on Google Cloud Run using GitHub Actions and Workload Identity Federation.
A technical step-by-step tutorial on building a serverless RAG knowledge base using BigQuery Vector Search and Vertex AI Gemini for enterprise data.
A technical step-by-step tutorial on deploying a private, GPU-accelerated LLM server using Ollama secured behind an Nginx authentication proxy.
Chunk size is usually chosen once and never questioned, because questioning it seems to need a full evaluation. It needs thirty questions, one number, and a sweep that runs without a single call to a language model.
Every API response states exactly how many tokens it cost, in four separate classes. Logging that number instead of estimating it is twenty lines – and it is the only basis on which a budget can be more than a warning.