#engineering
Explore public prompts for engineering. Narrow the topic by medium or change the order.
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5 Prompt Engineering Techniques That Actually Move the Needle (XML Beats Markdown by 28%)
Five quantifiable techniques from an AI engineer's hundreds of hours testing across GPT-4, Claude, and Gemini: CoT scaffolding, persona + goal + anti-goal, XML being 28% more accurate than markdown, negative examples outperforming positive ones, and prompt chaining beating the mega-prompt.
AI Agent Harness Engineering: Separating the LLM from the Deterministic Orchestration Layer
Mitchell Hashimoto's "Agent = Model + Harness" framework. The reason 88% of enterprise AI agents never reach prod isn't the prompt — it's the missing deterministic orchestration layer. Four CS primitives you can't skip: state machine, idempotency, DAG, DLQ.
Flip Your AI Dev Workflow: Generate the Prompt First, Then the Code (4 Phases)
Instead of telling AI to write code directly, have it translate a vague goal into an executable prompt structure first, then use that structure to drive code generation. Phase 1 Intent → Phase 2 Structuring → Phase 3 Tool-Level → Phase 4 Implementation.
Onboard New Hires to a Codebase with Gemini
Upload the whole repo or its key files, and Gemini gives new hires a reading order to understand the codebase within an hour
Run an Architecture Review on Your Whole Codebase with Claude
Upload the repo and Claude returns an architectural map, the top 5 fixes to prioritize, and the file paths to back them up.