Every AI project reaches the same meeting. Someone says "the output isn't right — should we fine-tune?" and the room splits into people who think fine-tuning is magic and people who think it's never worth it. Both are wrong, and the difference between them is a diagnosis nobody made.
This free course teaches the practical half of taking an LLM to production: when fine-tuning is genuinely the answer, how it works without the training mathematics, and everything after — LoRA, quantization, serving, evaluation, monitoring and cost. It is the topic that turns "I've built a chatbot" into "I've run one", and it is what interviewers ask when they want to know whether you have shipped anything.
What this course covers
Two halves that belong together. The first five lessons are fine-tuning: when it is the right tool, what it actually changes, how to build a dataset that doesn't teach the model to hallucinate, and the two techniques — LoRA and quantization — that put it within reach of a single GPU.
The last five are LLMOps: running the thing. How to evaluate a model honestly, how to serve it, what to monitor, and how to keep the bill from quietly tripling. This half applies whether you fine-tuned anything or not — most production LLM systems never train a model and still need all of it.
💡 No training mathematics. No CUDA. No gradients derived by hand. Placement interviews ask what fine-tuning is for, when to choose it, and what goes wrong — not how backpropagation works.
The decision everyone gets wrong
Here is the single most valuable idea in the course, and it fits in one line: facts → retrieval, behaviour → fine-tuning, constraints → prompting.
A chatbot inventing your company's refund policy does not need fine-tuning — it needs the policy retrieved and an instruction to answer only from it. A model that writes correct summaries in the wrong format doesn't need fine-tuning either; it needs a length rule. Fine-tuning earns its cost in a narrower case than most teams assume: a behaviour you can demonstrate with thousands of examples, at a volume where paying for few-shot examples on every request has become real money.
Lesson 1 gives you a five-minute experiment that settles the argument in most meetings.
A first look at a fine-tune
Modern fine-tuning is anticlimactic to look at. A dataset file, a config, a job:
{"messages": [
{"role": "system", "content": "You write maintenance tickets."},
{"role": "user", "content": "The AC in meeting room 3 is dripping onto the carpet."},
{"role": "assistant", "content": "{\"category\":\"HVAC\",\"location\":\"Meeting room 3\",\"urgency\":\"high\",\"summary\":\"AC unit leaking water onto carpet\"}"}
]}job = client.fine_tuning.jobs.create(
training_file=train_id,
validation_file=val_id,
model="<base-model-version>",
hyperparameters={"n_epochs": 2},
)Result
Two hours of compute. And none of that was the hard part — the hard part was the four weeks of building and reviewing those examples, and the evaluation that proves the result is better than a good prompt. Lessons 3, 6 and 7 are about exactly that gap between "the job succeeded" and "we should ship this".
Why learn this for placements
Because it is where AI interviews go once the definitions are out of the way. "What is RAG?" is the warm-up; "when would you fine-tune instead of using RAG?" is the question that actually separates candidates — and it is reported from TCS, Infosys, Wipro, Accenture and Cognizant rounds constantly.
Product companies push further into operations: how would you evaluate it, how would you roll it out, what would you monitor, how would you halve the cost. Those are ordinary engineering questions wearing AI clothes, and they reward anyone who has thought about a system rather than a demo.
Pair this with the company exam guides when you're preparing: TCS NQT and Cognizant among others.
How this course works
Ten lessons, read in order. Casual explanation first, the technical term afterwards, diagrams where the idea is structural, and real numbers wherever a claim is made — because "it uses less memory" is useless next to "13B at 4-bit is about 6.5GB of weights".
Each lesson ends with a Practice Zone: six MCQs from real selection rounds with the company logos attached, plus two hands-on tasks — reading a training log, planning a dataset, sizing hardware, designing an evaluation. Attempt each one before revealing.
Course roadmap
| # | Lesson | What you walk away with |
|---|---|---|
| 1 | When to Fine-Tune | the diagnosis: prompting vs RAG vs fine-tuning, and the hidden costs |
| 2 | How Fine-Tuning Works | what changes in the model, epochs, overfitting, catastrophic forgetting |
| 3 | Preparing Training Data | how many examples, what a good one looks like, leakage and splits |
| 4 | LoRA & PEFT | adapters, rank, QLoRA — and why ten variants cost one model in memory |
| 5 | Quantization Explained | 4-bit and 8-bit, the memory arithmetic, and what it costs in quality |
| 6 | Fine-Tuning in Practice | a project end to end, rollout, versioning, and when to stop |
| 7 | Evaluating LLMs | eval sets, LLM-as-judge, groundedness, and regression testing |
| 8 | Serving & Deployment | KV cache, continuous batching, TTFT, hosted vs self-hosted |
| 9 | LLMOps & Monitoring | what to log, what to alert on, and the drift you can't see |
| 10 | Cost & Latency | caching, routing, batch tiers — halving a bill without hurting quality |
Company-wise PYQs
After lesson 10 the sidebar continues into company pages — reported questions with full answers: TCS, Infosys, Wipro, Accenture, Amazon, Microsoft and Google.
How to study this course
Read lessons 1–3 carefully even if you never intend to train anything — the decision framework and the dataset thinking are what get asked. Lessons 4 and 5 are the technique names you need to recognise. Lessons 7–10 apply to every LLM application, fine-tuned or not.
If you have one evening, read lessons 1, 7 and 10. The RAG-versus-fine-tuning decision, how you'd evaluate a model, and how you'd cut cost are the three questions that come up most often — and all three are answerable without ever having run a training job.
🎯 If you have built anything at all — even a small project — bring numbers. "I compared a prompted baseline against a LoRA fine-tune on a 200-case eval set and the prompt won, so we shipped the prompt" is a stronger answer than most people give, precisely because it ends in not fine-tuning.
FAQ
Do I need a GPU to follow this course?
No. Everything here is conceptual and decision-oriented, and the code examples are short enough to read. If you do want to try a real fine-tune, a hosted fine-tuning API needs no hardware at all, and QLoRA on a free cloud notebook GPU is enough for a small experiment.
Is fine-tuning still relevant when models keep getting better?
The need for it keeps shrinking at the low end — things that required a fine-tune in 2023 are a prompt now. But the cases that remain are real: consistent behaviour at high volume, a specialised domain voice, and running a smaller, cheaper model at a bigger model's quality on one narrow task. Knowing when not to fine-tune is the more valuable half of the skill.
How is this different from the RAG course?
The RAG course is about giving a model facts it doesn't have. This one is about changing how it behaves, and about operating whatever you built. They're complementary — lesson 1 here is largely about telling them apart, and real systems usually use both.
What should I learn before this?
Ideally the Generative AI course (tokens, context windows, what a model is doing) and the Prompt Engineering course, since "have you tried a better prompt?" is the first question in every fine-tuning discussion.
Ready? Start here — Lesson 1: When to Fine-Tune →