AI WITH Rithesh

Why LLMs needed Reinforcement Learning

How did a next-word predictor become an assistant that follows instructions, refuses harm, and reasons through olympiad math? 

The answer is reinforcement learning and this series explains every landmark, from the RLHF recipe behind ChatGPT to the verifiable-reward RL behind DeepSeek-R1.

14 standalone videos. Watch any one on its own, or go in order. Every episode is built the same way: plain-English intuition first, then the math derived from
first principles, TWO simple worked examples you can follow on paper, the failure modes that bite in practice, and a head-to-head comparison with the alternatives.


What the series covers:
• The alignment gap: why pre-training alone is not enough
• Reward models, policy gradients, PPO (the clip + the KL leash)
• Cheaper paths: RLAIF/Constitutional AI, DPO, rejection sampling
• The reasoning era: process rewards, o1, GRPO, DeepSeek-R1 / RLVR
• Agentic RL and how to choose a method for your own project

www.youtube.com/playlist?list...

1 day ago | [YT] | 5

AI WITH Rithesh

Anthropic just figured out how LLMs work!
Anthropic's interpretability team just published something remarkable: evidence that language models keep a small, privileged set of representations they can report, steer, and reason with sitting atop a much larger sea of automatic processing. It looks a lot like the "global workspace" that neuroscientists associate with conscious access in humans.They call this J-space
Checkout my video on J-space
https://youtu.be/uTUOGQtJ-uc

5 days ago | [YT] | 4

AI WITH Rithesh

Why LLMs needed Reinforcement Learning

How did a next-word predictor become an assistant that follows instructions, refuses harm, and reasons through olympiad math? The answer is reinforcement learning and this series explains every landmark, from the RLHF recipe behind ChatGPT to the verifiable-reward RL behind DeepSeek-R1.

14 standalone videos. Watch any one on its own, or go in order. Every episode is
built the same way: plain-English intuition first, then the math derived from
first principles, TWO simple worked examples you can follow on paper, the failure
modes that bite in practice, and a head-to-head comparison with the alternatives.

No hand-waving and no LaTeX wall — every formula comes with real numbers.

What the series covers:
• The alignment gap: why pre-training alone is not enough
• Reward models, policy gradients, PPO (the clip + the KL leash)
• Cheaper paths: RLAIF/Constitutional AI, DPO, rejection sampling
• The reasoning era: process rewards, o1, GRPO, DeepSeek-R1 / RLVR
• Agentic RL and how to choose a method for your own project

www.youtube.com/playlist?list...

1 week ago | [YT] | 4

AI WITH Rithesh

The theory that AI/ML interviews at OpenAI, Anthropic, Meta, Google, Apple, Microsoft, Netflix, and Tesla actually test — one animated, narrated deep dive per question. No hand-waving: every episode derives the idea from first principles, shows a runnable code snippet, animates the intuition, and ends with exactly what the interviewer is probing for.

This playlist covers the 16 fundamentals every ML engineer is expected to own cold: the bias–variance decomposition and double descent, parametric vs non-parametric models, the curse of dimensionality, linear and logistic regression from their likelihoods, why the MLE of variance is biased, MLE vs MAP vs Bayesian inference, generative vs discriminative models, L1 vs L2 regularization, convexity and non-convex training, why gradient descent works (and fails), backprop by hand, entropy/cross-entropy/KL, bagging vs boosting, VC dimension, and universal approximation with no free lunch.

Each video is standalone with its own intro and takeaway — start anywhere, or watch in order for a full theory refresher. New clusters (Classical ML, Deep Learning, ML System Design, and more) drop regularly.

▶ Watch in order, or jump to the question you're weakest on.
🔔 Subscribe for the rest of the AI/ML Engineering Interview series.

#MachineLearning #MLInterview #MLEngineer #InterviewPrep #DataScience #DeepLearning

www.youtube.com/playlist?list...

2 weeks ago | [YT] | 4

AI WITH Rithesh

If you're prepping for an ML, GenAI, or backend interview, embeddings and vector databases come up first - and they sound simple until you have to explain why dense beats sparse, or what a vector database does that Elasticsearch can't.

This beginner tier opens with six BACKGROUND questions (the history and the why), then nine foundations - each with a concrete code sample (sentence-transformers, FAISS, gensim, numpy). By the end you'll be able to explain what an embedding really is, how
it's trained, why all three similarity metrics agree on normalized vectors, and what "approximate" search buys you.

What you'll learn:
- Why we moved from TF-IDF to dense embeddings, and a short history of the field
- The distributional hypothesis, static vs contextual embeddings
- What a vector database adds over an inverted index and a NumPy array
- How embeddings are trained, hard negatives, similarity metrics, ANN, evaluation

https://youtu.be/p5RbP5y-_2o

3 weeks ago | [YT] | 2

AI WITH Rithesh

"Strong on definitions. Light on tradeoffs."
Picture the candidate who walks out of a GenAI interview sure they crushed it. They defined RAG, embeddings, agents, fine-tuning, and LLM eval cold.
Then came the second question: "Okay, but why does that happen, and what would you do about it?"
Silence.
That second question is what quietly decides GenAI interviews right now. So I built an entire course around surviving it.
It collects the toughest, most commonly asked questions across the five pillars of modern GenAI engineering and answers each one the way a strong candidate actually should:
→ Derive the mechanism instead of naming it
→ Put a number on it: bytes, recall, latency, GPU memory
→ State the tradeoff you're accepting out loud

Five modules, three tiers each (Beginner foundations → Intermediate mechanisms → Architect-level system design):
🔹 RAG: hybrid search, Reciprocal Rank Fusion, re-rankers, "lost in the middle," and designing retrieval for 500M+ docs under 200ms.
🔹 Embeddings & Vector DBs: anisotropy, HNSW, product quantization, Matryoshka embeddings, sharding to a billion vectors, zero-downtime re-indexing.
🔹 Agents & Agentic Systems: the agent loop, tools, memory design, MCP, multi-agent orchestration, long-horizon autonomy, security.
🔹 Fine-Tuning: SFT vs alignment, LoRA/QLoRA/DPO, the GPU memory math, ZeRO and FSDP, serving many fine-tunes at once.
🔹 Evaluation: LLM-as-a-judge and its failure modes, faithfulness and context precision/recall, benchmark contamination, catching silent quality decay in prod.

Every answer comes with background, a worked example, and real code. The trickier claims are checked against the original papers.

It's for anyone targeting AI Engineer, ML Engineer, Applied Scientist, or the newer LLM/GenAI Engineer roles, and for practitioners who'd rather understand the machinery than copy recipes.

One tip: try to answer each question out loud before watching. The spots where you get stuck are the useful ones.

Course link here: www.udemy.com/course/genai-engineer-interview-prep…

Checkout the early bird prices valid for next 5 days

If a GenAI question has ever ended an interview for you, drop it below. I'm collecting the ones that keep coming up.

Note : This is a paid course on Udemy. This course is a collection of my Youtube Videos along with some additional content. If you prefer an ad free course like experience checkout the course

3 weeks ago | [YT] | 5

AI WITH Rithesh

Hey everyone! 👋

I am working to build a more engaged community of people genuinely interested in educational content. If you have been inactive or no longer watch our videos, I' would appreciate it if you could unsubscribe.

This helps me focus on connecting with viewers who are actively learning and engaging with my r channel. It keeps my metrics honest and helps us understand our real audience better.

If you have just been busy or lost interest but want to jump back in, no worries—you can always re-subscribe anytime! But if you are not engaged, hitting unsubscribe helps us both out.

Thanks for understanding! ✌️

4 weeks ago | [YT] | 6

AI WITH Rithesh

LLM Fine-Tuning: The Complete Interview Masterclass (Beginner → Architect)

Everything you need to understand and to interview on LLM fine-tuning, in one place.

This series takes you from the absolute fundamentals to production systems, built entirely from first principles with clear visuals, named papers, and concrete worked examples. It's designed for AI/ML engineers preparing for interviews, and for anyone who fine-tunes models and wants to actually understand the machinery instead of copying recipes.

Three tiers, designed to be watched in order:

PART 1 — BEGINNER: The Mental Model
Pretraining vs SFT vs alignment, loss masking, chat templates, catastrophic forgetting, LoRA vs full fine-tuning, quality vs quantity, reading a loss curve, and choosing hyperparameters. The foundations everything else builds on.

PART 2 — INTERMEDIATE: The Mechanisms
Why LoRA actually works, the alpha/r scaling, QLoRA (NF4, double quantization, paged optimizers), DPO vs RLHF, the KL/reference "leash," reward hacking, mixed precision, gradient checkpointing, the memory math, sequence packing, and evaluation beyond loss.

PART 3 — ARCHITECT: The Systems
Finetune vs RAG vs tools, distributed training (DDP/ZeRO/FSDP, tensor & pipeline parallelism), Mixture-of-Experts, long-context extension (YaRN), multi-LoRA serving, quantization for serving, data flywheels, reproducibility, model merging, the alignment tax, fine-tuning's effect on safety, and distillation.

WHO THIS IS FOR
Aspiring and current AI/ML engineers, data scientists, ML platform/infra engineers, and researchers — whether you're prepping for interviews or building real fine-tuning systems.

HOW TO USE IT
Start with Part 1, even if you're experienced — each tier assumes the mental model from the one before. Pause and try to answer each question out loud before hearing the answer; your stumbles are exactly the gaps an interview will find.

New videos and tiers are added here — subscribe and save the playlist so you don't miss
updates.

#LLM #MachineLearning #AIEngineer #FineTuning #LoRA #DeepLearning #MLInterview #GenAI #MLOps
www.youtube.com/playlist?list...

4 weeks ago | [YT] | 4

AI WITH Rithesh

RAG Interview Masterclass: Zero to Architect
Everything you need to ace Retrieval-Augmented Generation questions in AI / ML engineering interviews — built as a structured, three-level course.
Most RAG content stops at "retrieve, then generate." This series goes deep: every question is built in layers — the background, the solution, an animated worked example, a technical deep-dive, the state-of-the-art frontier, and exactly what the interviewer is really testing.
📺 The path:
▶️ Part 1 — Beginner (7 questions)

Why RAG exists, parametric vs non-parametric knowledge, semantic vs keyword search, chunking, embeddings & cosine similarity, prompt anatomy, precision vs recall.
▶️ Part 2 — Intermediate (10 questions)

Hybrid search & reciprocal rank fusion, cross-encoder re-rankers, RAG evaluation, lost-in-the-middle, HyDE, chunk granularity, multi-hop retrieval, metadata filtering, embedding migration.
▶️ Part 3 — Architect (10 questions)

ANN index design, scaling to 500M docs, index freshness, observability, agentic vs GraphRAG, multi-tenancy, hallucination under knowledge conflict, cutting cost 70%, high-stakes domains, and structured documents.
By the end you'll be able to discuss RAG from first principles all the way to production system design — with the depth and named techniques (SPLADE, RankGPT, GraphRAG, ScaNN, HNSW, conformal prediction, and more) that separate strong candidates.
Watch in order, or jump to your level. New to RAG? Start with Part 1.
#RAG #AIEngineer #MachineLearning #LLM #GenAI #InterviewPrep #VectorSearch #MLOps
www.youtube.com/playlist?list...

1 month ago | [YT] | 4

AI WITH Rithesh

Even wonder how to make a large language model faster at inference without sacrificing output quality?

It is one of the toughest engineering challenges in deploying AI at scale.

I have put together a detailed visual explainer that builds up the answer from first principles, covering the various techniques behind today's fastest LLM serving systems like:
Speculative decoding - producing multiple tokens per expensive forward pass
KV-cache compression - quantization, eviction, and low-rank methods to tame memory growth
Model compression - quantization, pruning, distillation, and Mixture-of-Experts
Serving architecture - continuous batching, PagedAttention, and prefill/decode disaggregation
FlashAttention - the kernel level optimization underpinning the rest

Each concept is animated for intuition, then connected back to the production stack: vLLM, SGLang, and TensorRT-LLM.

1 month ago | [YT] | 2