Newline AI Accelerator

TAKE THE NEXT STEP

In this free consultation call, you'll discover:

Attention Is All You Need

The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely.

Understanding LLMs

Fine-Tuning Guide

BERT: Pre-training of Deep Bidirectional Transformers

We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models.

Retrieval-Augmented Generation for Knowledge-Intensive Tasks

Large pre-trained language models have been shown to store factual knowledge in their parameters, and achieve state-of-the-art results when fine-tuned on downstream NLP tasks. However, their ability to access and precisely manipulate knowledge is still limited, and hence they perform less well on knowledge-intensive tasks. We introduce RAG models where the parametric memory is a pre-trained seq2seq model and the non-parametric memory is a dense vector index of Wikipedia, accessed with a pre-trained neural retriever.

RAG Systems

Prompt Engineering

GPT-4 Technical Report

We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks. We evaluate GPT-4 on a diverse set of benchmarks, including simulating exams that were originally designed for humans. GPT-4 substantially improves over GPT-3.5.

Chain-of-Thought Prompting Elicits Reasoning

We explore how generating a chain of thought—a series of intermediate reasoning steps—significantly improves the ability of large language models to perform complex reasoning. In particular, we show that by prompting the model to generate a coherent series of short sentences that mimic the reasoning process a person might have when responding to a question, language models can solve challenging tasks that otherwise would be beyond their capabilities. Experiments demonstrate gains across arithmetic, commonsense, and symbolic reasoning tasks.

LangChain Tutorial

Vector Databases

LLaMA: Open and Efficient Foundation Language Models

We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters. We train our models on trillions of tokens, and show that it is possible to train state-of-the-art models using publicly available datasets exclusively, without resorting to proprietary and inaccessible datasets. In particular, LLaMA-13B outperforms GPT-3 (175B) on most benchmarks, and LLaMA-65B is competitive with the best models, Chinchilla-70B and PaLM-540B.

Attention Is All You Need

Understanding LLMs

Fine-Tuning Guide

BERT: Pre-training of Deep Bidirectional Transformers

Retrieval-Augmented Generation for Knowledge-Intensive Tasks

RAG Systems

Prompt Engineering

GPT-4 Technical Report

Chain-of-Thought Prompting Elicits Reasoning

LangChain Tutorial

Vector Databases

LLaMA: Open and Efficient Foundation Language Models

Why Most Engineers Get Stuck in AI

Stop wasting months bouncing between YouTube tutorials, outdated courses, and academic papers. We'll reveal the hidden trap that keeps even experienced developers from making real progress in AI.

The Fastest Path to Becoming the "AI Person"

Learn the proven roadmap to position yourself as the go-to AI engineer inside your company. Gain the practical expertise required to lead projects, land $200K+ promotions, and secure long-term career security.

Promoted to Research Engineer at $440K!

After months of dedicated work on our AI initiatives, I'm excited to share that I've been promoted to Research Engineer. Grateful for the opportunity to lead cutting-edge ML projects...

Sep 8

Our AI-Powered SaaS Platform Hit $2.4M ARR!

What started as a side project 18 months ago has grown into a 7-figure business. Here's what I learned about building AI products that customers actually pay for...

Oct 15

Thanks AI! Slashed Operating Expenses by $1M.

Proud to share that the document processing system my team built has reduced manual review time by 87%. This translates to massive cost savings and faster turnaround times...

Nov 2

Turn Your Existing Skills into Real World AI Projects

Discover how to quickly repurpose the skills you already have to build real-world AI projects that recruiters, hiring managers, and investors are looking for right now.

🔒 claimstriage.ai

📋 Claims Triage

📄 Resume Scorer

🎓 Learning Companion

🎬 Video Editing

♙️ Chess Coach

🏗️ Forklift Safety

Claims Triage AI

Automated claims processing with 92% accuracy

📄

Drop PDF claims here for instant processing

✓

CLM-2847

Auto accident • $12,450 • Policy match: 98%

Approved

⚠

CLM-2848

Property damage • $8,200 • Needs review

Review

✓

CLM-2849

Medical • $3,850 • Policy match: 95%

Approved

PROCESSING TREND

Success Rate

92%

Avg. Response

780ms

Cost/Session

$0.028

Monthly ROI

$17.8k

AI Resume Scorer

Analyze and optimize your resume in seconds

📄

Upload your resume (PDF or DOCX)

Resume Score

Top Suggestions:

💡

Add quantifiable metrics to your achievements (e.g., "Increased sales by 45%")

💡

Include relevant keywords: machine learning, Python, TensorFlow

💡

Reorganize work experience in reverse chronological order

SKILL STRENGTH ANALYSIS

ATS Match

94%

Keywords

18/25

Readability

A+

Format

9.2/10

AI Learning Companion

Personalized tutoring for any subject

Progress

Accuracy

Completion

🤖

Great job on completing Chapter 3! Let's dive into neural networks. What aspect would you like to explore first?

👤

Can you explain backpropagation in simple terms?

🤖

Think of it like training a basketball player. When they miss a shot, they adjust their technique. Backpropagation is how neural networks "adjust their technique" by calculating errors backwards through the network layers.

AI Video Editor

Automated video editing with intelligent scene detection

👤

REC

06:09.2

TRANSCRIPT Auto-generated

[00:00] I'm a very experienced software engineer. I have been in the industry for over 25 years.

[00:04] I started my work mostly on the low level stuff as an embedded systems engineer, operating systems, device drivers, et cetera.

[06:09] And later moved up the stack to distributed systems and I really enjoyed working on full stack applications...

TIMELINE

0:00 0:02 0:04 0:06 0:08 0:10 0:12 0:14 0:16 0:18 0:20 0:22 0:24

📹

00:06.0 / 00:09.2

124%

CAPTIONS

Auto-generate

Accuracy: 98.2%

SCENES

Auto-detect

14 scenes found

Duration

0:24

Words/min

142

Quality

Export

MP4

AI Chess Coach

Personalized chess training with real-time analysis

BEST MOVES

Ke7

By bringing the King to the center immediately, you maximize your long-term winning chances. You keep the position flexible, allowing you to react to White's mistakes. You don't commit your pawns yet, which gives White fewer chances to "lock" the position into a draw.

This move is good, but it forces a change in the pawn structure immediately. If White responds correctly (e.g., by taking or supporting), it might simplify the position too quickly. In endgames, simplifying often helps the defender (White) achieve a draw. You want to keep the tension on the board, which is why Ke7 has the higher statistical win rate.

POSITION ANALYSIS

Material: White +2

Position: Winning

Game Phase: Endgame

FOCUS AREAS

B+P ENDGAME

CONVERSION

KING ACTIVITY

SKILL IMPROVEMENT

ELO Rating

1458

Win Rate

62%

Games

147

Accuracy

87%

Forklift Safety AI

Real-time warehouse safety monitoring system

CAMERA FEED: ZONE A-4 LIVE

FORKLIFT

WORKER

PALLET

ACTIVE ALERTS

⚠

Worker in path

Distance: 2.4m | Speed: 4km/h

✓

Safety vest detected

Compliance: 100%

ZONE STATUS

Active Forklifts: 3

Personnel: 12

Hazard Level: Medium

TODAY'S SUMMARY

0 INCIDENTS

47 WARNINGS

8.2 HRS

INCIDENT PREVENTION RATE

Cameras

Uptime

99.8%

Response

180ms

Prevented

142

Supply Chain Optimizer

AI-powered logistics workflow optimization

Optimized

In Progress

EFFICIENCY IMPROVEMENT

Total Time

22 Days

Time Saved

6 Days

Cost Reduction

27%

Carbon Impact

-18%

Data ETL Pipeline

AI-powered data cleaning and transformation

ID	Customer	Email	Revenue	Date	Status
001	John Smith	[email protected]	$45,200	2024-01-15	✓ Valid
002	Jane Doe	invalid-email	$32,800	2024-01-16	⚠ Error
003	Bob Johnson	[email protected]	$58,900	2024-01-17	✓ Fixed
004	Alice Williams	[email protected]	$41,500	2024-01-18	✓ Valid
005	NULL	[email protected]	$29,300	2024-01-19	⚠ Error

Records

2,450

Errors Found

147

Auto Fixed

139

Accuracy

94.5%

ABOUT US

Meet Your Instructors

Zao Yang isn't just another AI instructor — he's an entrepreneur who's been building and scaling products for over a decade. As the co-creator of Farmville and founder of Newline, an online education company for developers, he's helped over 250,000 engineers level up their careers.

Today, Zao is at the forefront of AI — building real applications and teaching senior engineers how to translate their skills into the AI economy.

✓

Co-creator of FarmVille: reached over 200 million users and generated $3 billion in revenue

✓

Founder of Newline: an education company trusted by 250,000+ software engineers worldwide.

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AI builder & Investor: actively building AI applications and has invested in over 130+ startups.

Dr. Dipen Bhuva is an AI/ML researcher with 150+ citations and 16 published research papers. He has three tier-1 publications, including Internet of Things (Elsevier), Biomedical Signal Processing and Control (Elsevier), and IEEE Access. In his research journey, he has collaborated with NASA Glenn Research Center, Cleveland Clinic, and the U.S. Department of Energy for various research projects. He is also an official reviewer and has reviewed over 100 research papers for Elsevier, IEEE Transactions, ICRA, MDPI, and other top journals and conferences. He holds a PhD from Cleveland State University with a focus on large language models (LLMs) in cybersecurity, and also earned a master's degree in informatics from Northeastern University.

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PhD in AI/ML: Cleveland State University with focus on large language models (LLMs) in cybersecurity

✓

150+ Citations: 16 published research papers with three tier-1 publications (Elsevier, IEEE Access)

✓

Research Collaborations: worked with NASA Glenn Research Center, Cleveland Clinic, and U.S. Department of Energy

MASTER AI

AI Accelerator Curriculum

WEEK 1

Onboarding & Tooling

Get started with AI development essentials. Set up your Python environment, learn Jupyter Notebooks, and explore the AI ecosystem including Hugging Face, hardware options (GPUs, TPUs), and productivity tools.

AI Onboarding & Python Essentials

• Course overview and community setup
• Python installation and virtual environments
• Basic Python: variables, data types, loops & functions
• Using Jupyter Notebooks

Introduction to AI Tools & Ecosystem

• Models & features: choosing the right AI model
• Finding and using AI models & datasets on Hugging Face
• Hardware for AI: GPUs, TPUs, and Apple Silicon
• Practical tips on productivity with AI

Orientation — Technical Kickoff

• Jupyter & Python setup for AI workflows
• Arrays, vectors, and tensors with NumPy
• Mathematical foundations: exponentiation, normalization
• Statistics and real data practice with Pandas

WEEK 2

AI Projects and Use Cases

Explore the landscape of LLM projects and understand the building blocks. Learn about different AI application types including RAG, vertical models, agents, and multimodal apps.

Navigating the Landscape of LLM Projects

• Transformer-based LLMs vs diffusion models
• Core LLM application types: RAG, vertical models, agents
• LLM use cases across industries
• Project scoping and identifying viable ideas

Building Your First LLM Application

• How inference works in LLMs
• Five phases of model lifecycle: pretraining to RLHF
• Working with Hugging Face, Modal, and vector databases
• Build "Hello World" LLM inference API

Intro to AI-Centric Evaluation

• Metrics and evaluation design
• Foundation for future metrics work
• Building synthetic data for AI applications

WEEK 3

Prompt Engineering & Embeddings

Master prompt engineering from structure to evaluation. Learn about tokens, embeddings, and how AI understands text, images, and audio across different modalities.

Prompt Engineering — Structure to Evaluation (Mini Project 1)

• Foundational prompt styles and structured formatting
• Building evaluators to measure LLM output quality
• Write "LLM-as-a-judge" prompts
• Advanced prompting techniques and A/B testing
• Experiment with DSpy for structured prompting

Tokens, Embeddings & Modalities

• Journey from raw text → tokens → embeddings
• Compare tokenizers: BPE, LLaMA, GPT-2, T5
• Word2Vec embeddings and vector math
• Apply to multimodal models: CLIP, ViLT, ViT-GPT2
• Byte Latent Transformers architecture

WEEK 4

Multimodal + RAG Systems

Deep dive into multimodal embeddings with CLIP and build retrieval-augmented systems. Learn contrastive learning and implement advanced RAG techniques.

Multimodal Embeddings (CLIP)

• Joint image-text representations with contrastive learning
• CLIP similarity queries and shared embedding space
• Build text-to-image and image-to-image retrieval
• Visual question answering (VQA) and image captioning
• Compare architectures: CLIP, ViLT, ViT-GPT2

RAG & Retrieval Techniques (Mini Project 2)

• Full RAG pipeline: pre-retrieval, retrieval, post-retrieval
• Vector databases, chunking, and query optimization
• Generate synthetic data using LLMs (Instructor, Pydantic)
• Baseline vector search with LanceDB and OpenAI embeddings
• Rerankers and statistical validation with t-tests

WEEK 5

Classical Language Models

Build n-gram language models from scratch and understand classical approaches. Learn 2-tuple loss embedding fine-tuning for search and ranking applications.

N-Gram Language Models (Mini Project 3)

• Bigram and trigram extraction using sliding windows
• Construct frequency dictionaries and probability matrices
• Sample random text and evaluate with entropy & NLL
• One-hot encode and build PyTorch neural networks
• Compare classical vs. neural model performance

2-Tuple Loss Embedding Finetuning (Mini Project 4)

• 2-tuple based embedding adaptation
• User-to-music & e-commerce use cases
• Contrastive learning and cosine similarity
• Search and ranking applications

WEEK 6

Attention & Fine-tuning

Understand self-attention mechanisms and build transformer layers from scratch. Master instructional fine-tuning with LoRA for domain-specific tasks.

Building Self-Attention Layers

• Motivation for attention: limitations of n-gram models
• Query, Key, Value mechanics and weighted sums
• Implement self-attention in PyTorch
• Visualize attention heatmaps using real LLMs
• Build single-head and multi-head transformers
• Implement Mixture-of-Experts (MoE) attention

Instructional Finetuning with LoRA (Mini Project 5)

• Difference between fine-tuning and instruction fine-tuning
• When to apply fine-tuning vs IFT vs RAG
• Lightweight tuning: LoRA, BitFit, prompt tuning
• Apply to case studies: HTML generation, resume scoring
• Use Hugging Face PEFT tools for training

WEEK 7

Architectures & Multimodal Systems

Explore feedforward networks, loss-centric training, and multimodal fine-tuning. Learn how to adapt CLIP for specialized classification and regression tasks.

Feedforward Networks & Loss-Centric Training

• Role of linear + nonlinear layers in neural networks
• MLPs refine outputs after self-attention
• Compare activation functions: ReLU, GELU, SwiGLU
• LayerNorm, positional encoding, skip connections
• Build intuition for depth, context, and structure

Multimodal Finetuning (Mini Project 6)

• CLIP and contrastive learning for image/text alignment
• Fine-tune CLIP for classification and regression
• Add heads on CLIP embeddings for downstream tasks
• Apply LoRA to vision/text encoders
• Diffusion models and video generation architectures

WEEK 8

Assembling & Training Transformers

Build a complete transformer architecture from scratch. Master advanced RAG techniques and learn from production-grade systems.

Full Transformer Architecture (From Scratch)

• Connect all core components: embeddings, attention, FFN
• Implement skip connections and positional encodings
• Use sanity checks and test loss to debug assembly
• Observe transformer behavior on structured prompts
• Compare with earlier trigram and FFN models

Advanced RAG & Retrieval Methods

• Production-grade RAG systems: Relari and Evidently
• Common RAG bottlenecks and solutions
• Compare embedding models and reranking strategies
• Evaluate using recall, MRR, and qualitative techniques
• RAG design for different use cases

WEEK 9

Specialized Fine-Tuning Projects

Apply fine-tuning to real-world scenarios: insurance claim processing and math reasoning. Learn tool-augmented fine-tuning with symbolic reasoning.

CLIP Fine-Tuning for Insurance

• Fine-tune CLIP for car damage classification
• Generate labeled datasets with Google Custom Search API
• Apply PEFT with LoRA and optimize with Optuna
• Evaluate accuracy using cosine similarity
• Deploy in insurance agent workflow using LLaMA

Math Reasoning & Tool-Augmented Finetuning

• Use SymPy for symbolic reasoning in LLMs
• Fine-tune with Chain-of-Thought (CoT) data
• Two-stage finetuning: CoT → CoT+Tool integration
• Evaluate using symbolic checks and semantic validation
• Train quantized models with LoRA

WEEK 10

Advanced RLHF & Engineering

Master preference-based fine-tuning with DPO, PPO, RLHF, and GRPO. Reverse engineer AI code agents like Copilot and Cursor.

Preference-Based Finetuning — DPO, PPO, RLHF & GRPO

• Why base LLMs are misaligned and preference correction
• Understand differences: DPO, PPO, RLHF, and GRPO
• Generate math-focused DPO datasets
• Apply ensemble voting to simulate majority correctness
• Compare training pipelines: cost, control, complexity

Building AI Code Agents — Copilot, Cursor, Windsurf

• Reverse engineer modern code agents
• Transformer context windows vs RAG + AST systems
• Indexing, retrieval, caching, incremental compilation
• Knowledge graphs and execution-aware completions
• Design multi-agent AI IDE stack

WEEK 11

Agents & Multimodal Code Systems

Master agent design patterns including tool use, planning, reflection, and collaboration. Build text-to-SQL and text-to-music architectures.

Agent Design Patterns

• Tool use, Planning, Reflection, Collaboration patterns
• Evaluation challenges: output variability, partial correctness
• Single-agent vs constellation/multi-agent architectures
• Memory models and tool integration
• Compare agent toolkits: AutoGen, LangGraph, CrewAI

Text-to-SQL and Text-to-Music Architectures

• Implement text-to-SQL with structured prompts
• Train and evaluate SQL generation accuracy
• Text-to-music pipelines: prompt → MIDI → audio
• Compare contrastive vs generative learning
• Evaluation tradeoffs for logic-heavy vs creative outputs

WEEK 12

Deep Internals + Production Pipelines

Study advanced transformer optimizations from DeepSeek-V3. Learn the complete LLM production chain from inference to deployment.

Positional Encoding + DeepSeek Internals

• Why self-attention requires positional encoding
• Compare types: sinusoidal, RoPE, learned, binary, integer
• Study skip connections and layer norms
• DeepSeek-V3: MLA, MoE, MTP, FP8 training
• When to use advanced transformer optimizations

LLM Production Chain (Inference, Deployment, CI/CD)

• End-to-end LLM production: data, serving, latency, monitoring
• Multi-tenant LLM APIs and vector databases
• Tradeoffs: hosting vs APIs, inference tuning
• Plan scalable serving stack
• LLMOps roles, workflows, and production tooling

WEEK 13

Enterprise LLMs & Career Growth

Master RAG hallucination control for enterprise search. Prepare for AI engineering roles with interview prep and career strategies.

RAG Hallucination Control & Enterprise Search

• RAG in enterprise settings with citation engines
• Hallucination reduction: constrained decoding, retrieval, DPO
• Evaluate model trustworthiness for sensitive applications
• Production examples: legal, compliance, finance contexts

Career Prep — Roles, Interviews, and AI Career Paths

• AI roles: Engineer, Model Engineer, Researcher, PM, Architect
• FAANG/LLM interview prep: DSA, behavioral, portfolio
• Mock interviews and story crafting with ChatGPT
• Build standout AI resume, repo, and demo strategy
• Internal AI projects, indie hacker paths, transition guides

WEEK 14

Career Growth & Staying Current

Learn how to stay current with AI research, news, and tools. Track foundational trends and understand the evolution of AI technologies.

Staying Current with AI (Research, News, and Tools)

• Track foundational trends: RAG, Agents, Fine-tuning, RLHF, Infra
• Tradeoffs: long context windows vs retrieval pipelines
• Compare agent frameworks: CrewAI, LangGraph, Relevance AI
• Real 2026 GenAI use cases: productivity + emotion-first design
• Stay current: newsletters, YouTube, community tools

SUCCESS STORIES

What Developers Are Saying About the AI Accelerator

One of the things that I've noticed from going to countless university courses is that you end up falling behind because the content is not recent and you also lack mentorship. With this bootcamp, I was able to talk to people in the industry who are actively working in data science or ML and they helped me think about problem-solving differently whether it was with ML or generative AI. I have been able to use the skills I learned in this course to work on and solve generative AI problems at my current job, which couldn't be possible without this course.

Anup Vasudevan

Senior Software Engineer · Capital One

The bootcamp is very deep and I got a lot out of the lectures, coaching sessions, and the case studies. I also got a lot out of working with other participants in the boot camp and bouncing ideas off each other. It's not just presentations but it was actually code and exercises and homework with pretty deep exercises. So I think one of the things that you get out of the boot camp is not only theory, but you also have to build and ship products. And that's what this boot camp prepares you for.

Jeff O'Connell

Senior Software Engineer

I recently completed the Full-Stack AI Bootcamp, and I can confidently say it exceeded all my expectations. If you're serious about learning AI engineering in depth, then this is the bootcamp to join, from start to finish.

Percy Brea

Senior Software Engineer · BlueTread

What I liked the most about the course is the project coaching. I like the fact that you get help with projects. I like the encouragement of being in a community of people that are all doing the same thing and kind of working towards a common goal. I thought the content would be totally overwhelming, but it really wasn't that bad. It was something that I could do and was really cool.

Chris Westbrook

Software Engineer

I was looking for a place to learn more about AI that wasn't just piecing together random things from the Internet. I wanted a more structured course with an approach that didn't feel like going back to college, and I found this. I liked that we had the live lectures, and then they were also recorded, so I could go through them twice. That helped sync some of the information in that I could watch a lecture. It was definitely a lot of content. But I feel like it was manageable with also the learning management system that was implemented like the quizzes and the notebooks.

James Newman

Tech Lead · Home Chef

I think the instructors have done a really good job curating the content to help you understand AI and also build out your own tools. If you're like myself and don't have a background in machine learning or anything like that, learning about AI can be really daunting, particularly because many of the concepts could be very dense. So I think they've structured the course content in a way that really allows you to understand the underlying concepts in AI, walking you through some of their early language models and you get to build out ngram LLMs all the way to modern day architectures.

Moses Valle-Palacios

Policy Director · Prev. Maryland GOC

I really loved the lectures. They were very detailed and thorough. I got to learn a lot of concepts from there and then I like the classwork and homework, because that gave me a lot of hands-on with proper guidance, like how I should be proceeding with that, etc. And the idea and the concept of creating our own project was the best thing, because then I could come up with my own idea, and I got the guidance from the instructors to build what I really wanted. Not only did I learn what I wanted to learn, but I also built something in which I could use my own prior experiences.

Ruby Jha

Engineering Manager · State Street

The course is well structured and the content is very elaborate... The teaching is very good. The team is really knowledgeable and they are experts in the field. There are a lot of hands-on exercises and we are doing a live project which we could use in production. That's really helpful in making your understanding concrete and you can get really confident with programming Gen AI. I'm very happy with this course. It helped me learn the concepts very clearly and I'm confident that I can develop any kind of AI applications.

Sunjith Sukumaran

Co-Founder

Transform Your Career

Join the Next Generation of AI Engineers

Don't miss this opportunity to master production-level AI engineering. Limited spots available for our next cohort. Apply now and start building the future.

FAQ

Frequently Asked Questions

How is this different from other AI Courses/Bootcamp?

Our program offers a unique approach by balancing practical AI programming skills with a deep understanding of foundational model concepts. Many other AI programming bootcamps focus exclusively on specific areas like RAG or fine-tuning and do not delve into foundational model concepts. Our curriculum is crafted to cover AI programming while incorporating essential foundational model concepts, giving you a well-rounded perspective and the skills to approach AI with a strong theoretical foundation. To our knowledge, few, if any, bootcamps cover foundational models in a way that empowers students to understand the entire AI model lifecycle, adapt models effectively, and confidently pursue project ideas with guided support.

What is the course structure?

Total Weekly Time Commitment: Approximately 3 hours for structured activities, including 2 hours of lectures and a dedicated 2-hour Q&A office hours and project coaching session. Hands-On Programming: Expect to dedicate 2–4 hours for practical programming exercises. Individual Project Work: The time spent on your project is up to you, so you can invest as much as you wish to build your skills. All sessions will be recorded for those unable to attend live, ensuring that no one misses valuable content.

Do I need any pre-requisite?

You need to be able to program and have a commitment to do the work and ask questions. You would need some Python programming, which would help with just a basic YouTube course (We can help you guide on it if you have 0 zero experience in Python). You don't need to do an ML course. You do need to be able to program and debug if you're concerned about the content of the course.

Do you have a guarantee?

We have a guarantee that we'll help you build your AI project, considering the scope and 3-month time period. This means we need to align on the project, the budget, and your time commitment. We'll need your commitment to be able to work on the project. For example, RAG-based, fine-tuning, building a small foundational model is totally within the scope. If you want to build a large foundational model, the project will have to focus on the smaller one first.

What is the target audience?

The goal is around 3 personas: (1) Someone who wants to apply RAG and instructional fine tuning for private on premise data at work. (2) Someone who wants to be able to fine tune a model to build a vertical foundational model. (3) Someone who wants to be able to use the AI knowledge for building and leading internal company project, consulting and build AI startups.

Will you be covering multi modal applications?

Yes. We'll be covering this and learning how to do fine tuning within this space as well. We will not be teaching video, but we will be covering: Text + SQL, Text + code, Text + voice, and Text + music.

Given how rapidly AI is evolving, how "future-proof" are the skills we'll learn?

We teach evergreen concepts that will allow you to participate in the AI conversation (tokenization, embeddings, vector databases, RAG, fine tuning). We provide historical context and mental models on different technologies to understand the evolution and where things will likely go. We provide deeper understanding of concepts that are important so that it internalizes (like Attention and key-query-value structures). We also provide conceptual understanding of state-of-the-art developments and why they matter. The combination of expandable evergreen concepts with applicability to current state of the art models will allow for a range that enables you to be fully grounded in the AI field both in concept and base level skills.

What specific support channels will be available outside lecture hours?

You'll have access to: Community platform with AI-enabled chat, Forum Q&A, Multiple group coaching calls, Notion workspace, and Email support with our operations manager and curriculum team. We anticipate that the support of the projects will extend beyond the curriculum. That's why we have different support channels for both the material, project, and discussion of the current AI news and any deconstruction.

What were the results from previous bootcamps?

People were able to build: Domain-specific coding platforms for local businesses, Facebook Marketplace item condition detector/classifier for arbitrage, "Chat with sermons" for churches, Document processing for insurance claims, Invoice processing for nonprofit (saved 10 hours/week), Calorie/macro counting application for ethnic cuisine, AI tutor, Resume scoring/generator system, Customer service application with video detection, Commercial real estate assessment using AI, Learning companion for courses, Legal aide assistant for legislative process, Personalized job search website, and Text to guitar tabs generative AI.

Ready to become an AI in 14 weeks?

In this free consultation call, you'll discover:

Why Most Engineers Get Stuck in AI

The Fastest Path to Becoming the "AI Person"

Turn Your Existing Skills into Real World AI Projects

Meet Your Instructors

AI Accelerator Curriculum

Onboarding & Tooling

AI Onboarding & Python Essentials

Introduction to AI Tools & Ecosystem

Orientation — Technical Kickoff

AI Projects and Use Cases

Navigating the Landscape of LLM Projects

Building Your First LLM Application

Intro to AI-Centric Evaluation

Prompt Engineering & Embeddings

Prompt Engineering — Structure to Evaluation (Mini Project 1)

Tokens, Embeddings & Modalities

Multimodal + RAG Systems

Multimodal Embeddings (CLIP)

RAG & Retrieval Techniques (Mini Project 2)

Classical Language Models

N-Gram Language Models (Mini Project 3)

2-Tuple Loss Embedding Finetuning (Mini Project 4)

Attention & Fine-tuning

Building Self-Attention Layers

Instructional Finetuning with LoRA (Mini Project 5)

Architectures & Multimodal Systems

Feedforward Networks & Loss-Centric Training

Multimodal Finetuning (Mini Project 6)

Assembling & Training Transformers

Full Transformer Architecture (From Scratch)

Advanced RAG & Retrieval Methods

Specialized Fine-Tuning Projects

CLIP Fine-Tuning for Insurance

Math Reasoning & Tool-Augmented Finetuning

Advanced RLHF & Engineering

Preference-Based Finetuning — DPO, PPO, RLHF & GRPO

Building AI Code Agents — Copilot, Cursor, Windsurf

Agents & Multimodal Code Systems

Agent Design Patterns

Text-to-SQL and Text-to-Music Architectures

Deep Internals + Production Pipelines

Positional Encoding + DeepSeek Internals

LLM Production Chain (Inference, Deployment, CI/CD)

Enterprise LLMs & Career Growth

RAG Hallucination Control & Enterprise Search

Career Prep — Roles, Interviews, and AI Career Paths

Career Growth & Staying Current

Staying Current with AI (Research, News, and Tools)

What Developers Are Saying About the AI Accelerator

Anup Vasudevan

Jeff O'Connell

Percy Brea

Chris Westbrook

James Newman

Moses Valle-Palacios

Ruby Jha

Sunjith Sukumaran

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Frequently Asked Questions