‘Nurturing’ AI: The Story of How an AI Copilot Was Built
summary
Check Point launches two AI copilots months ago and reports strong customer adoption while describing the engineering: a patented system that auto-generates LLM‑optimized API descriptions so prompts fetch only relevant APIs, an AI Firewall that uses an LLM to moderate another LLM, and an LLM Manager that routes queries to the best model. Development relies on Few‑Shot and Chain‑of‑Thought prompting, a QA Bot that checks semantic equivalence, SQL generation to query logs instead of leaking data, and a vision demo that turns a whiteboard into firewall rules.
AI-written summary · about
chapters (11)
- 0:00AI Copilot journey
- 1:30Why build AI Copilot now
- 4:25Raising AI with prompts
- 8:44Choosing AI model sibling
- 12:34Securing AI with AI Firewall
- 16:29Teaching AI with prompt engineering
- 21:16Optimizing API descriptions
- 25:41Testing AI Copilot with QA Bot
- 27:33Future of AI Copilot
- 31:19Call to action for AI Copilots
- 34:12Q&A
related talks
20mAI in Action at Check Point
47mSafeguard your copilot with Azure AI
45mThe era of the AI Copilot
01h 53mAgentic AI: Build a Multi-Agent Application with CrewAI
12mA conversation with Charles Lamanna on building custom copilots with Microsoft
27mLarge-scale Enterprise Co-pilots - Making AI Useful for Business
48mMastering the AI Shift: Adaptive Strategies for Software Evolution
15mAI Studio - Creating and scaling your custom copilots