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Field SOP

Scenario-based tutorials with real screenshots, prompts, workflow templates and pitfalls.

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AI Test Generation SOP: Safely Drafting Unit, Regression Tests

A six-step SOP for AI automated test generation: when to use + baseline (coverage/defect escape) -> unit tests from code -> regression tests from issues -> batch boundary/parameterized cases -> test maintenance (stale assertions/redundancy) -> human gate + CI. Each step ships copyable pytest and jest code, plus 5 pitfalls (loose assertions/interdependencies/fake coverage/mock drift/not testing the real path) and 5 FAQ. Iron rule: AI only drafts, human review + CI green before merge.

Agent Observability & Online Eval SOP: Tracing, Cost, Alerts

A complete SOP for production AI Agent observability and online evaluation in six steps: defining the baseline (latency/token cost/tool success rate/error rate), distributed tracing (Langfuse @observe decorator + Phoenix register + OpenTelemetry spans), token and cost tracking, tool-call artifact logging, online evaluation (LLM-as-judge automated scoring + user thumbs feedback via create_score), and alerting with iteration (threshold rules + dashboards). Each step includes copyable Python code, 6 pitfall entries, and 5 FAQ items. Open-source tools first; LangSmith mentioned as a SaaS option.

DeepSeek-Reasonix Terminal Coding Agent Setup SOP

DeepSeek's prefix cache makes long-session costs bifurcate (0.02 yuan hit vs 1 yuan miss, a 50x gap), but general agents do not optimize for DeepSeek's cache. This SOP walks through DeepSeek-Reasonix (community-built, unofficial) end-to-end: npm install, reasonix setup, reasonix.toml config sketch, cache-aware maintenance, executor+planner dual-model, and MCP plugins. All commands from the README; config fields per official docs.