知识库
Artificial Intelligence & Next-Gen LLMs
Practical guidance on choosing, deploying, and governing modern large language models across products and enterprises.
该详细指南目前仅提供英文版。
10 主题 · 10 篇指南
- Inflection Pi vs Claude vs ChatGPT
Pi, Claude, and ChatGPT serve different jobs: Pi leans into personal, empathetic conversation, Claude into careful long-form reaso…
- Open Source AI for Enterprise: Llama
Meta's Llama family put capable open-weight models within reach of any engineering team. Self-hosting gives you control over data,…
- Retrieval-Augmented Generation for Enterprise
Retrieval-augmented generation grounds model answers in your own documents, cutting hallucinations and keeping knowledge current w…
- AI Agents and Autonomous Swarms
AI agents go beyond chat by planning, using tools, and acting toward a goal. Swarms coordinate many agents on larger tasks. The pr…
- LLM Red Teaming and Prompt Injection
Prompt injection is the SQL injection of the LLM era: untrusted text can override your system instructions and make the model do t…
- Small Language Models at the Edge
Small language models bring AI to phones, browsers, and embedded devices without a network round trip. They trade raw capability f…
- Multimodal AI Pipelines
Multimodal systems combine text, images, audio, and video in one flow. The hard part is not calling a vision model; it is aligning…
- Synthetic Data Generation with AI
Synthetic data fills gaps where real data is scarce, private, or expensive. Generated examples can bootstrap classifiers, test edg…
- AI Governance and the EU AI Act
The EU AI Act sorts AI systems by risk and sets obligations for each tier. Governance is not paperwork after the fact; it is how y…
- Million-Token Context Windows
Million-token context windows let you feed whole codebases or document sets into a single prompt. That power comes with cost, late…