LLMs, agents, RAG, and AI engineering — a continuously updated tech column.
After reading, you will get a seven-category failure attribution table, three acceptance checklists, and autonomy thresholds. Follow the steps to run a read-only pilot first, then decide which actions to automate.
After reading, you will get a checklist of runtime observability fields for agents and an error-tier rollback table: start with low-traffic logging, then gradually grant more authority.
After reading, you will be able to build a post-launch closed loop for AI agent monitoring, alerting, human takeover, rollback, and evaluation, and use a two-week pilot to decide whether to continue, degrade, or stop.
This article provides three diagrams—task flow, permission flow, and evidence flow—and a launch checklist to help developers determine whether an Agent can stably enter production and roll back safely.
After reading, you can turn vague requirements into a task contract that specifies inputs, outputs, tool permissions, failure budgets, and rollback lines, so you can start the launch review today.
This article reviews system prompts, Few-shot examples, and structured output methods in prompt design from an engineering perspective, and provides a practical checklist, common pitfalls, and directions for further reading.
From an engineering implementation perspective, this article reviews the core issues of large model safety and alignment. It outlines layered defenses, red-team testing, and launch checklists for hallucination control, jailbreak prevention, and content moderation, helping developers build more reliable generative AI applications.
This article walks developers through the fundamentals of large language models: how Transformers process text with attention, how context windows shape input and output budgets, and how to craft prompts that express tasks consistently. It includes a practical checklist, common pitfalls, and directions for further reading.
This article explains three foundational concepts of large language models for developers: the attention mechanism in Transformers, the engineering boundaries of context windows, and prompt design methods, with a practical checklist and common pitfalls.
This article outlines three foundational concepts for developers: how Transformers use attention to process text, how context windows constrain the information a model can see, and how prompt engineering improves output stability through structured input. It also provides a practical checklist and common pitfalls.
By the end, you'll have a task closed-loop table and a rollback checklist—pick a low-risk scenario right away and complete an Agent pilot validation in three days.
After reading, you get a business event-sharding table, a failure-recovery checklist, and pre-launch validation steps to complete a controlled Agent pilot in two days.
After reading, you can get the task admission table, permission boundary table, and acceptance table, and use a seven-day pilot to determine which operations can be automated, require human approval, or must be disabled.
Based on public information, this article maps control points for agent planning, memory, and tools into a read-only, confirmation, restricted, and disabled acceptance checklist.
Use a three-column boundary table and a ☐ read-only pilot checklist to determine which agent planning, memory, and tool-call actions can be automated, which require approval, and which should be unavailable.