LLMs, agents, RAG, and AI engineering — a continuously updated tech column.
After reading, you can organize MCP tool integration boundaries according to public specifications. Today, start by building a read-only tool catalog, an FC invocation table, and a rollback checklist.
Today, you can set up an idempotent chain in four stages: generation, review, draft, and release, and run a rollback drill once to avoid duplicate releases and dirty data.
For RAG, semantic search, and knowledge base scenarios, this article presents an engineering method, checklist, and common pitfalls for selecting embedding models based on Chinese semantic adaptation, vector dimensions, inference cost, and evaluation metrics.
From an engineering perspective, this article breaks down the three core capabilities of AI customer service: understanding intent, completing slots, and graceful fallback with human handoff. It also provides actionable workflows, checklists, and common pitfalls.
This article reviews how to combine knowledge graphs with large models from an engineering perspective. It focuses on entity extraction, relation reasoning, and question answering integration, covering implementation steps, common pitfalls, and evaluation methods for developers and AI practitioners.
From an engineering perspective, this article outlines the key paths to accelerating large-model inference: how quantization balances accuracy and memory usage, how vLLM improves throughput and KV Cache utilization, and the constraints and selection methods for edge inference. It also provides a practical deployment checklist and common pitfalls.
This article starts from the engineering requirements of semantic search and RAG, compares the applicable boundaries of Milvus and pgvector, outlines embedding, indexing, hybrid retrieval, and evaluation workflows, and provides a pre-launch checklist and common pitfalls.
Deploying large models is not just about prompts and RAG; safety and alignment are systems engineering. This article organizes risk boundaries, controls, and actionable checklists along three main themes—hallucination, jailbreaking, and content moderation—to help developers build auditable generative AI applications.
From an engineering perspective, this article breaks down prompt design, focusing on the applicable scenarios for system prompts, Few-shot examples, and structured output. It provides a practical checklist, common pitfalls, and testing recommendations to help developers improve the stability and parseability of large model outputs.
This article outlines the key path for privately deploying large models from an engineering implementation perspective: how to choose hardware based on GPU memory, concurrency, and cost; how to use containers and orchestration systems to deliver stable inference services; and how to establish a monitoring and alerting system for GPU and business metrics.
From voice input to model response to speech playback, a real-time conversation system is not simply ASR, LLM, and TTS stitched together. This article outlines pipeline modules, latency budgets, streaming chunking, interruption control, and an engineering checklist to help developers build more natural voice AI products.
A multi-agent system is not simply stacking multiple large models; it requires clear responsibility boundaries, communication pathways, and conflict arbitration. From an engineering perspective, this article reviews key methods for role design, message buses, and conflict resolution, and provides actionable checklists and common pitfalls.
A developer-oriented overview of three fundamentals of large language models: Transformer attention, context-window limits, and prompt design, with a practical checklist, common pitfalls, and directions for further reading.
For RAG and semantic search scenarios, this article presents a practical embedding model selection checklist and pitfall avoidance advice from three perspectives: semantic quality, vector dimensions, and inference and storage cost.
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.
A developer-oriented overview of three key concepts in large language models: the probabilistic prediction mechanism of Transformers, the engineering constraints of context windows, and prompt design methods, with a practical checklist and common pitfalls.
This article is for developers and AI practitioners, outlining three foundational concepts in large language models: the attention mechanism of Transformers, context-window limits, and prompt design. It also provides a practical checklist, common pitfalls, and directions for further reading.
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.
After reading, you can generate a 2026 Q1 candidate framework comparison table by scenario and use a two-week read-only pilot to validate cost, integration, permissions, and governance risks.
After reading, you will receive boundary acceptance checklists for planning, memory, and tools, and learn how to use a read-only pilot to judge which steps can be executed automatically, avoiding permission overreach and memory contamination.
After reading, you will get a comparison table of MCP and Function Calling, a three-step read-only pilot plan, and selection conclusions to decide which tools and permissions to integrate first.
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.