You can already ship software. But "add AI to it" now means RAG pipelines, agents, tool calling, evals, and guardrails, and most tutorials stop at a toy demo that falls apart in production. This 8-week live cohort is the hands-on path: you learn each building block, then build it the same session, week after week, until you ship a real capstone you can put in front of an interviewer.
Built for engineers moving into AI Engineer and Forward-Deployed Engineer roles · 8 hands-on weeks · 3 capstone tracks
Most engineers don't struggle with AI because they lack intelligence. They struggle because every tutorial stops at "hello world" and never shows the production reality.
"My RAG demo works on 5 docs, then dies in prod."It looked great in the notebook, but real corpora, bad chunks, and irrelevant retrievals crater the accuracy the moment users show up.
"I can call an LLM, but I can't build an agent."You know the API. You still can't wire up tool calling, a reasoning loop, and structured outputs into something that reliably gets work done.
"I glued a LangChain tutorial together and it breaks."When it fails you have no idea why, because you never learned evals, tracing, or how to reason about the context window.
"Everyone name-drops MCP, evals, guardrails."You keep hearing the terms in job descriptions but have never actually built one, so interviews expose the gap fast.
Every concept is followed by hands-on code the same session. That's how it becomes a skill, not a bookmark.
One core idea at a time, in a deliberate sequence: LLM basics, retrieval, agents, evals. Each week builds on the last.
The same session, you write the code: a RAG pipeline, a tool-calling agent, an MCP connection, an eval suite. Theory turns into a working artifact while it's fresh.
As your toolkit grows, you combine them: advanced retrieval, multi-agent orchestration, guardrails and observability wrapped around real workloads.
The final weeks you scope, build, and present a production-shaped project end-to-end, with eval results and a demo you can show off.
Live sessions, Sat & Sun, 9:00–11:30 AM IST. Prerequisites: prior coding experience and a basic understanding of software systems. Tap any week to expand. Every week = learn the theory, then build it in a hands-on lab.
The vocabulary and the provider tradeoffs before you build
The 5-stage pipeline that grounds an LLM in your data
Breaking through the accuracy ceiling of naive RAG
Advanced patterns, and when not to build RAG at all
Turning an LLM into an agent that gets work done
Orchestration, routing, and memory across agents
The densest week: making agents accurate, safe, and observable
Scope it, build it, present it
Pick one and ship it end-to-end. Each track is a production-shaped system, not a toy demo.
Deterministic personal-finance AI agents with tool-calling ReAct, typed SQL execution, a hybrid-search knowledge base, and a reversible pseudonymization gate for 0% raw PII leakage to the LLM.
Root-cause attribution for product funnels using a cyclic, stateful multi-agent system with a dedicated "Falsifier" agent, Pydantic AI tools, typed SQL via DuckDB, and hybrid BM25 + cross-encoder reranking.
Transparent scholarship-eligibility agents combining retrieval over compliance PDFs, active web search, and structured JSON rulesets to generate cited eligibility reasoning.
By week 8, "add AI to it" stops being vague and becomes a set of engineering decisions you can make and defend.
One price, full access, no upsells.
Every Sat & Sun for 8 weeks, recorded if you miss one.
You write real code each session, from first API call to full pipelines.
Choose FinQuery, Product Discovery Copilot, or GrantMatch and ship it.
Evals, guardrails, prompt-injection defense, tracing, and serving patterns.
Direct doubt-clearing over the cohort group, never stay stuck.
Architecture review and 1:1 feedback on your project plan and demo.
"I'd shipped backend for years but froze on anything AI. Building a RAG pipeline and an agent the same session I learned them is what finally made it click."
"The RAG-vs-long-context week alone saved me from over-engineering a pipeline my project didn't even need. Very production-minded."
"MCP, evals, guardrails were just buzzwords to me before. Now I've actually built them and can talk through the tradeoffs in interviews."
"The capstone is the part that mattered. I walked out with a real project and eval numbers to show, not another certificate."
Small cohort · personal attention · limited seats.
…you could be the engineer who ships a real RAG-and-agents system with evals and guardrails, while everyone else is still stuck at the tutorial demo. Reserve your seat for the next cohort.