Curious engineer. Public notebook. Simple bytes.
I break complex AI systems into simple notes, diagrams, and projects.
I read engineering posts, build small systems, draw the flows, and share what I learn about RAG, agents, evals, workflows, and the messy parts of making AI useful.
6 years 5 months building software across cloud, data, and AI-adjacent systems. This site is where I turn that learning into notes for engineers, students, and teams.
Production AI system map
How I connect product, data, agents, and release readiness
A production AI feature is not one model call. It is a workflow that needs trust, control, evaluation, and observability.
Product request
User intent, workflow context, permissions, and success criteria.
UX + state layer
Copilot screens, review queues, progress states, corrections, and trust signals.
Workflow orchestration
APIs, auth, queues, state machines, retries, and human approval paths.
RAG + data trust
Documents, metadata, retrieval, citations, freshness, and permission filters.
Model + agent control
LLM calls, tool contracts, routing, fallback logic, and bounded agent actions.
Eval + observability
Traces, prompt versions, golden workflows, cost, latency, and release gates.
Learners and professionals taught or mentored through sessions and workshops
Institutional teaching contexts including AICTE ATAL FDP, DSCE, and MIT Pune
Flagship AI workflow project used to explain system design trade-offs
Public notebook of AI notes, diagrams, checklists, and project breakdowns
How to use this site
Courses, blogs, and projects for learning AI engineering without the fog.
I use this site like a public notebook: I read, build, break concepts apart, and turn them into simple notes people can reuse.
Follow a course
Start with AI System Design when you want a course-like path instead of scattered links.
Open →BlogRead deep blogs
Read one focused idea at a time: RAG trust, context layers, agent workflows, and evaluation.
Open →ProjectStudy a project
Open the Agentic AI Production Harness when you want to inspect how the pieces connect.
Open →ChecklistUse a checklist
Apply review checklists before trusting an AI feature in a real workflow.
Open →The formats I want to keep repeating
Strong personal sites feel useful because readers know what they will get. These are the formats I want this site to be known for.
Latest from my notebook
Start with what I am actively turning into notes.
Strong learning happens when ideas are small enough to inspect. These are the first places I would send a new visitor.
AI System Design Course
Course-style notes for interviews and real system design: RAG, agents, memory, evals, security, infrastructure, and case studies.
Open →BlogRAG Copilot Build Note
How I would build a RAG copilot starting from user workflow, evidence, citations, evaluation, and feedback loops.
Open →VisualsAI Engineering Diagrams
Visual diagrams for explaining system flows, RAG trust, workflow risk, and AI product architecture.
Open →Glimpse of Sessions
Trusted in classrooms, faculty programs, and hands-on AI workshops.
AICTE ATAL FDP delivery, full-stack GenAI workshop sessions, national-level AI/ML training, mentoring, and judging.
SMVITM, Udupi
AICTE ATAL FDP Resource Person
Faculty Development Program on Modelling for Business Decisions with certificate and classroom.
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Dayananda Sagar College of Engineering
Full-Stack GenAI Application Development
Hands-on GenAI application development workshop with mentoring, project discussion, and judging.
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MIT Arts, Commerce & Science College, Pune
National-level AI & ML Workshop
Two-day national-level AI & ML workshop with event and session photo evidence.
View moreStart path
I want to learn AI system design
Start with the course and move chapter by chapter.
Start path
I want to see how you think
Read the blog and look for diagrams, checklists, mistakes, and trade-offs.
Start path
I want to inspect your work
Open the projects and resources instead of reading a claim about expertise.
Useful work, not loud claims
A few things worth inspecting first.
Agentic AI Production Harness
A project page for explaining controlled agent workflows, traces, evaluation, and review points.
Inspect →AI Feature Shipping Checklist
A practical checklist for reviewing RAG quality, workflow risk, logging, fallback behavior, cost, and release readiness.
Inspect →Context Layer Reading Note
A reading note on why trusted context, expert review, and health signals matter in internal AI assistants.
Inspect →