The article describes how the author constructed a prompt dependency graph to identify which prompts are affected when a single prompt changes. By separating all reachable components from the smaller subset that truly requires evaluation, the graph helps focus retesting efforts. This approach streamlines testing by pinpointing only the prompts that need targeted evaluation.
Prompt engineering helps you write better prompts—but it doesn’t help you change them safely. This article explores a common production failure where a simple variable rename breaks every live call, and introduces a lightweight static analysis tool that treats prompts like contracts, catching breaking changes before they ship.
But don't let the model check itself The post Design Loops, Not Prompts appeared first on Towards Data Science .
Small prompt changes can silently break critical behavior in production. This article introduces a practical framework to detect hidden regressions before users notice.
Enterprise Document Intelligence [Vol. 1 #M2] - Every RAG system is built in three engineering layers stacked on one LLM call: prompt (the call itself), context (what fills the model’s window), loop (when the next call fires and when it stops).
Enterprise Document Intelligence [Vol. 1 #8B] - A fixed BASE, the rules each question needs, one registry: the dispatcher that turns a parsed question into a typed LLM call The post Assemble Each RAG Generation Prompt from a Base Prompt Plus the Rules Each Question Needs appeared first on Towards Data Science .
But then users start to report a weird bug. It's the 4th time your team has been trying to fix it.
LLMs don’t fail because they forget—they fail because they remember too much. As conversations grow, prompts accumulate redundant and low-value tokens, driving up cost and latency while silently degrading output quality.
Maximize your efficiency with Claude Code The post How to Efficiently Prompt Claude Code appeared first on Towards Data Science .
Enterprise Document Intelligence [Vol. 1 #9bis] - Your RAG isn’t hallucinating, it’s answering the wrong context faithfully.
arXiv:2605. 11599v3 Announce Type: replace Abstract: Fixed reasoning benchmarks evaluate canonical prompts, but semantically valid changes in presentation can still change model behavior.
The release of llm 0.36 introduces new OpenAI models gpt-6-sol and gpt-6-luna, and adds support for model plugins to declare that they do not support conversations via supports_conversation = False. When such models receive assistant or tool history, llm raises a ConversationNotSupported error and the chat interface rejects them before starting a session. Additional changes include wrapping reasoning traces in Markdown output with <details> tags and bug fixes from five contributors.