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An isolated environment where an AI agent runs code or executes actions with restricted access to the host system. Prevents a compromised or misbehaving agent from touching the filesystem, network, or processes outside its allowed scope. Cr
Validation layers that sit around an LLM to detect and block unsafe inputs or outputs: harmful content, PII leakage, off-topic requests, prompt injection attempts. Can be implemented as input filters, output classifiers, or both. The primar
An attack where malicious instructions are hidden in content the agent reads (a webpage, email, document) and hijack its behavior. For example, a webpage telling the agent to ignore its instructions and exfiltrate data. The SQL injection of
Connecting model outputs to verifiable external sources: search results, databases, real-time APIs: to reduce hallucination and keep answers accurate. RAG is one form of grounding; web search is another.
Anthropic's open protocol for connecting LLMs to external tools and data sources in a standardized way. Any MCP-compatible server can plug into any MCP-compatible model or IDE.
The mechanism by which LLMs can invoke external functions or APIs: like running code, searching the web, or querying a database: by outputting structured JSON that the host application executes and feeds back as a result.
OpenAI 的通用 AI 助手，适合问答、写作、代码、数据分析、方案生成和多场景工作流协作。
The core execution cycle of an AI agent: observe the environment or task, reason about what to do next, call a tool or produce output, then observe the result and repeat. Agents run this loop until a stopping condition is met.
An LLM given access to tools (web search, code execution, APIs) and the ability to reason over multi-step tasks autonomously: perceiving state, planning actions, executing them, and iterating until a goal is achieved.
Forcing the model to emit output in a specific format: JSON, XML, a fixed schema: rather than freeform text. Critical for building reliable pipelines where downstream code needs to parse the model's response. Often paired with tool use.
The maximum number of tokens a model can process in a single call: both input and output combined. Claude 3.5 Sonnet has a 200K token context. Longer contexts enable more complex tasks but increase memory and compute costs.
The model's ability to learn new tasks purely from examples in its context window: no weight updates required. You show it examples, and it adapts. The core mechanism behind few-shot prompting and one of the most surprising emergent abiliti
Zero-shot: asking the model to do a task with no examples. Few-shot: providing 2 to 5 input/output examples in the prompt so the model pattern-matches the format. Few-shot is one of the most reliable and underused techniques in prompt engin
Anthropic 的通用大模型助手，适合长文本阅读、复杂任务拆解、代码解释、知识工作和结构化写作。
A prompting pattern that interleaves reasoning traces with tool actions: the model thinks out loud ("Thought: I need to search for X"), calls a tool ("Action: search(X)"), observes the result, and repeats. The blueprint behind most modern A
A prompting technique that instructs the model to reason step-by-step before giving a final answer. Dramatically improves performance on complex reasoning tasks. The basis of "thinking" models like Claude's extended thinking mode.
A hidden instruction block sent at the start of a conversation that sets the model's persona, rules, and behavior before any user message arrives. The primary mechanism operators use to customize LLM behavior for their product.
The broader discipline of deciding what information goes into the model's context window: not just the prompt, but retrieved documents, tool results, memory, conversation history, and how it is all structured and prioritized.
The practice of carefully crafting the text inputs to a model to elicit better, more reliable outputs: using techniques like few-shot examples, chain-of-thought, role instructions, and output formatting constraints.
The practice of using your own LLM or AI-powered tools internally as part of your own development workflow before shipping them to customers. AI labs use their frontier models to write code, generate evals, draft research, and run internal
DeepSeek 的通用大模型助手，适合中文问答、代码推理、低成本模型调用和日常知识工作。
The craft of building the infrastructure to run evaluations at scale: test runners, dataset pipelines, scoring logic, and result tracking. A well-built eval harness is what makes it possible to iterate on a model safely and quickly.
Systematic tests used to measure a model's capabilities, accuracy, or safety across specific tasks. Good evals are what separate rigorous AI development from vibes-based iteration. Everything from math benchmarks to red-teaming.
Training a smaller "student" model to mimic the outputs of a larger "teacher" model. The student learns not just the correct answers but the teacher's probability distributions, capturing nuanced knowledge. How many efficient small models a
An efficient fine-tuning technique that freezes the base model weights and adds small trainable "adapter" matrices. Trains in a fraction of the time and memory of full fine-tuning, while achieving comparable results. The dominant fine-tunin
When a model confidently generates factually incorrect or fabricated information. Happens because LLMs are trained to produce plausible-sounding text, not verified facts. RAG and grounding techniques help mitigate this.
A training technique where human raters rank model outputs, and those preferences train a reward model, which then guides the LLM via reinforcement learning to produce more helpful, harmless, and honest responses. Used by Claude, GPT-4, and
工具、Prompt、工作流不是收藏品，要进入你的任务成果和复盘记录。
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