Pavan Belagatti — AI Evangelist | Developer Advocate | Agentic Engineering | Speaker | Tech Content Creator | Ask me about LLMs, RAG, AI Agents, Agentic Systems & DevOps
AI Evangelist | Developer Advocate | Agentic Engineering | Speaker | Tech Content Creator | Ask me about LLMs, RAG, AI Agents, Agentic Systems & DevOps
Pavan Belagatti ranks #229 of 18,566 LinkedIn creators in Computer Software, and is a standout voice in India. They have 103.8K followers and published 40 posts in the last 60 days at a 0.1% average engagement rate.
- 103.8K followers
- 40 posts / 60d
- 0.1% avg engagement
- 482 follower growth / 30d
The roast
Pavan transitioned from marketing to developer just to prove he could be just as annoying in Python as he was in a PowerPoint deck. With 100,000 followers and an engagement rate lower than the odds of a junior dev actually reading his tutorials, he’s basically an API that returns 404s.
About Pavan
Pavan is an award winning tech evangelist. A pioneer in growth hacking from India, he is also an AI, DevOps, Data Science and Machine Learning enthusiast. He transitioned himself from a marketer to a self-taught developer to understand how developers think and write code. Now, he writes in-depth technical tutorials on various tech publications. He has over 11 years of experience in AI, developer evangelism, developer marketing, technical content creation and branding activities. He has contributed to some of the top tech platforms like The Linux Foundation, DZone, UpWork, ComputerWeekly, TheNewStack, TheNextWeb, TechinAsia, The Entrepreneur, etc. He has also spoken at various meetups and conferences on cloud-native topics and DevOps best practices. When he started his professional journey, Pavan was also recognized as one of the pioneers in the field of growth hacking in India. He was awarded 'DevOps Person of the Year' in 2020 by DZone. He loves tech storytelling and everything about cloud-native tech. He is on his journey to empower developers around the globe.
Highlights
- Top 1% Audience — 103,793 followers
- Top 1% in Computer Software — Ranked #33 of 4742 creators
- Top 5% in India — Ranked #9 of 755 creators
- Top 5% Creator — 40 posts in 30 days
Recent posts
This is How Agentic Search Systems Work at DoorDash, Instacart, and Uber Eats!👇 While they all integrated Large Language Models (LLMs) to solve complex search intents, their architectures look completely different: - DoorDash: Keeps retrieval mostly classical. They use LLMs offline to enrich an existing knowledge graph, employing an inverted RAG approach as a guardrail to safely parse queries at runtime. - Instacart: Splits the traffic, common queries hit a deeply context-engineered offline RAG cache, while cold-start "tail" queries are handled in real-time by a fine-tuned Llama-3-8B model.
53 reactions · 0 comments · 0 reposts
Your AI Agents are only as good as the CONTEXT you provide them. Yes! But...most engineering organizations are approaching AI agents the wrong way. They focus on the agent itself, while the real differentiator is the context layer behind the agent. As you can see in the diagram, a developer expresses an outcome - “Deploy Service X to production.” Instead of manually navigating dozens of tools, dashboards, policies, and approval workflows, an AI agent takes ownership of the task. But the agent's effectiveness doesn't come from the model alone. It comes from access to organizational context
39 reactions · 0 comments · 1 reposts
Developers are excited about AI agents & workflows BUT most miss one critical thing: CONTEXT. In agentic engineering, context isn't just important. It's everything. Port.io is built exactly for this reason, you can use it as a context layer, for workflow orchestration, agent management, and governance to build your AI-SDLC without losing control.
45 reactions · 2 comments · 1 reposts
Last updated 2026-08-01