Ravi Teja Palanki — AI Product Leader
“I'm the AI PM who turns the demo that wowed the room into the product that survives the quarter.”
THREE WORLDS.
One bridge.
When engineering says "we need a validation layer," design says "users need to feel in control," and business asks "what's the ROI at 10x scale" — I make each feel understood AND challenged, then synthesize a path that serves all three.
The Technical Layer
Shipped enterprise AI assistants with OpenAI APIs, multi-database RAG, and hallucination guardrails. Built eval frameworks for response accuracy in safety-critical industrial operations. Designed context engineering pipelines that survive production.
See the workThe Experience Layer
Coordinated 30+ cross-functional stakeholders across Android, iOS, head-mounted tablet, and Web. Designed progressive trust patterns with a human-centred lens because plant operators don't trust a system when their life is at stake.
See the workThe Strategy Layer
Potential $100M+ opportunity. Launched first Alpha 0-1 MES for battery gigafactories. Led consulting motions & pre-sales for product engineering services with 85% win ratio in the past.
See the workExperience
Shipped in regulated industries
Skills Built
PRODUCTS I SHIP
It is to become a world-class,
top 0.1% AI product leader —
My Profile
12+ years shipping enterprise AI at Fortune 100 scale. From 0-to-1 MES for battery gigafactories to Gen AI assistants for plant operators.
The career narratives, the products, and the journey.
Working Notes
112 deep dives across Evals, Agentic Systems, Harness Engineering, and the AI PM OS.
Written the night a pilot broke, a metric lied, or an assumption snapped — before the lesson got polished into a slide.
100 Key Takeaways
The hardest-won lessons from 100+ deep dives — distilled into one cinematic billboard.
One idea at a time. Slow enough to sit with. The 5% that compounds, separated from the 95% that doesn't.
Prompt ≠ Context
The Story
In June 2025, Tobi Lütke — the CEO of Shopify — posted a sentence on X that quietly changed the conversation. He wrote that the most important skill in AI is not prompting. It is context engineering: "the art of providing all the context necessary so that an LLM can work out a reasonable solution to the task at hand."
The line landed because it named something practitioners had been feeling for two years and could not put words to. Teams had poured millions into prompt optimisation — rewriting instructions, A/B-testing tone, tuning temperature. The instructions got sharper. The model got smarter. The product still misfired.
The team was optimising the 5% they could see. The failure lived in the 95% they weren't thinking about. That assembled block — instructions, retrieved knowledge, memory, tools, structure — is the context. The difference between an AI product that works reliably and one that fails unpredictably almost always lives there, not in the user's message.