A Short Lesson in Simpler Prompts (blog.nilenso.com)

The author of context-viewer describes a practical prompt-engineering journey: by designing the system to “work with LLMs instead of fighting them,” they reduced 300‑word brittle prompts to concise ~15‑word prompts that perform reliably. The breakthrough wasn’t clever wording but two engineering steps — segmentation (breaking assistant/user messages into meaningful chunks such as code blocks, files, instructions, PRDs) and categorization (labeling those chunks so models receive structured context). That shift lets small prompts reference well-structured context pieces, improving repeatability and making LLMs predictable building blocks rather than brittle oracles. Technically, the project pairs rule-based parsing and labels with LLMs for semantic tasks and uses a time-aware data model (changesets + snapshots) to represent entities over time. The spec advocates CRDT-style reasoning (LWW element set) and a simple SQLite schema for changes: entityId, timestamp, JSON value — enabling point-in-time views and replayable history. The stack is pragmatic: Remix + TypeScript, Prisma, SQLite (path to Postgres), D3 for charts, Datasette for read-only exploration, and Fly.io for deployment. For ML/engineering teams this demonstrates a repeatable pattern: robust pre-processing (segmentation + tags) + minimal prompts + principled state modeling = more reliable LLM integrations and easier scaling from prototypes to production.