learnwithparam Playbook Claude Code Best Practices Inside The setup this playbook builds Why Claude Code drifts on a long task Measure one real ticket before you change anything Write a CLAUDE.md that Claude follows Let it run without prompts, and never touch prod Turn the team's repeated workflow into a skill Send research and review to subagents Block risky edits with hooks, not instructions Package it, run it everywhere, and measure again Keep the setup current after you hand it over Cheatsheet Questions engineers ask about Claude Code Claude Code Best Practices Set up CLAUDE.md, permissions, skills, subagents, hooks and a plugin on one real repo, and measure the change.
Get the playbook learnwithparam Interview kit AI Engineer Interview Questions Inside The interview loop, mapped Your cold answer, scored Agent or workflow Tool calling and guardrails RAG over Acme's help docs and tickets Evals and failure modes Cost and latency under follow ups Level up and practise AI Engineer Interview Questions One system design question, design a support agent for an online shop, answered the way interviewers score it: agents, tools, RAG, evals, cost and latency.
Get the interview kit learnwithparam Playbook Cut LLM Costs in AI Agents Inside The bill one ticket runs up, and the route down Why an agent costs more than a chat Measure cost and quality per resolved ticket before you change anything Put the stable prefix first so every step reads it from cache Stop re-sending what the loop no longer needs Cap what the agent writes and how hard it thinks Send easy steps to a small model and keep hard tickets on the large one Move the work nobody waits for to the batch API Hold the line with an eval gate and a budget alarm Hand the numbers to the team that keeps them The cost levers on one page Questions engineers ask about LLM cost Cut LLM Costs in AI Agents Measure cost per resolved ticket, then cut it with caching, trimming, routing and batching, behind an eval gate.
Get the playbook learnwithparam Playbook Add LLM Guardrails to AI Agents Inside The guardrails between a ticket and a refund Map the failures and baseline the agent Cut what the agent is allowed to do Screen tickets and tool results before the agent acts Check every tool call before it runs Check the reply before the customer sees it Turn the red-team set into a CI gate Give the guardrails an owner in production The page to keep Questions people ask about LLM guardrails Add LLM Guardrails to AI Agents Least privilege, input and output checks, tool-call validation and red-team tests for a support agent that can refund and email.
Get the playbook learnwithparam Playbook Build Multi-Agent Orchestration Inside From one coding agent to a team that ships Why a team of agents fails more often than one agent Measure the single agent on one real issue Run the test for whether a second agent pays Pick the pattern: orchestrator-worker, pipeline, maker-checker or swarm Split it into planner, engineer and reviewer Scope each role's tools Write the hand-off contract and the caps Rerun the issue and debug the team against the baseline Hand it over with the pre-ship checklist and an owner The page to keep Questions engineers ask Build Multi-Agent Orchestration Split one coding agent into a planner, an engineer and a reviewer, with scoped tools, a hand-off contract and caps, then trace the team against the agent it replaced.
Get the playbook learnwithparam Playbook The AI architecture playbook Inside Workflow with one model step Single agent with tools Supervisor with workers Retrieval over your documents Durable agent with human approval Tools behind a gateway The AI architecture playbook Six shapes most production AI systems take, when each one fits, what it is made of and how it fails first.
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