AI — Understand the model, then ground it in your own data

The most common job going, and the one with the widest gap between a demo and something you would put in front of a customer. Understand what the model actually is, then learn to feed it your data without it inventing things.

Who this is for: You want to build products on top of LLMs.

3 tracks · 21 hours · one of three paths in the learning roadmap by K Laxman. An optional 4-hour prelude, Just enough to not be bluffing, covers the underlying ideas first.

Track 1 — LLM fundamentals

Understand the thing everything else sits on (~6 hours)

If you only ever do one item on this page, do the first one. Almost every confused conversation I have about AI systems traces back to someone never having watched this.

The whole pipeline, end to end

You can trace a token from raw internet text all the way to a chat reply, naming every stage.

pretraining · tokenisation · transformers · inference · SFT · RLHF · hallucination · tool use · reasoning models

Build this — Re-implement decoding. Take any provider that returns logprobs and implement greedy, temperature and top-p sampling yourself. Write a README explaining, in your own words, when you'd use each. It is thirty lines of code and it makes §0.5 permanent.

You're done when: You can explain, without notes, why a model hallucinates confidently rather than saying 'I don't know'.

Put to work in: Jarvis — six interchangeable providers behind one loop · CAMSCode — enterprise AI coding assistant

Track 2 — Retrieval-Augmented Generation

The part most people do badly (~10 hours)

Everyone can build a RAG demo in an afternoon. Almost nobody can tell you why theirs returns the wrong chunk. The difference between those two people is this track — and the eval set at the end of it.

RAG properly — all fifteen techniques

You can name, and choose between, the retrieval strategies. Not just 'embed, store, search'.

indexing · retrieval · generation · multi-query · RAG-Fusion · query decomposition · step-back prompting · HyDE · routing · query construction · multi-representation indexing · RAPTOR · ColBERT · CRAG · adaptive RAG

Build this — RAG over your own notes — plus a 30-question eval set. The bot is the easy half. The assignment is the eval set: thirty real questions with known answers, and a score you can quote. Without it you have a demo, and you will not be able to tell whether tomorrow's change helped.

You're done when: Given a wrong answer, you can separate retrieval failure from generation failure in under a minute.

Put to work in: CAMS Lens — SEBI-grounded compliance intelligence · Agentic Research & Decision Intelligence Assistant

Track 4 — Evals, tracing & observability

The stage everyone skips (~5 hours)

This is the one that separates a demo from a product. As a system gets complicated, evals and tracing stop being nice-to-have and become the only way you can change anything without breaking it. If you cannot state your system's accuracy as a number with a dataset behind it, you do not have a system — you have a vibe.

Dataset → evaluator → task → interpretation

You can build an evaluation harness before you build the feature.

datasets · evaluators · LLM-as-judge and its failure modes · tracing · regression detection

Build this — Put a number on the RAG bot. Add the eval set and tracing to what you built in Track 2. Report the score in the README. Then change the chunk size and report it again — that delta is the whole discipline.

You're done when: You can say what your system's accuracy is, with a number and a dataset behind it.

Put to work in: CAMS Lens — grounded answers that have to be right

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