AI bootcamp 2
This advanced AI Bootcamp teaches you to design, debug, and optimize full-stack AI systems that adapt over time. You will master byte-level models, advanced decoding, and RAG architectures that integrate text, images, tables, and structured data. You will learn multi-vector indexing, late interaction, and reinforcement learning techniques like DPO, PPO, and verifier-guided feedback. Through 50+ hands-on labs using Hugging Face, DSPy, LangChain, and OpenPipe, you will graduate able to architect, deploy, and evolve enterprise-grade AI pipelines with precision and scalability.
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Dr. Dipen
I am an AI/ML researcher with 150+ citations and 16 published research papers. I have three tier-1 publications, including Internet of Things (Elsevier), Biomedical Signal Processing and Control (Elsevier), and IEEE Access. In my research journey, I have collaborated with NASA Glenn Research Center, Cleveland Clinic, and the U.S. Department of Energy for various research projects. I am also an official reviewer and have reviewed over 100 research papers for Elsevier, IEEE Transactions, ICRA, MDPI, and other top journals and conferences. I hold a PhD from Cleveland State University with a focus on large language models (LLMs) in cybersecurity, and I also earned a master’s degree in informatics from Northeastern University.
zaoyang
Owner of \newline and previously co-creator of Farmville (200M users, $3B revenue) and Kaspa ($3B market cap). Self-taught in gaming, crypto, deep learning, now generative AI. Newline is used by 250,000+ professionals from Salesforce, Adobe, Disney, Amazon, and more. Newline has built editorial tools using LLMs, article generation using reinforcement learning and LLMs, instructor outreach tools. Newline is currently building generative AI products that will be announced soon.
01Remote
You can take the course from anywhere in the world, as long as you have a computer and an internet connection.
02Self-Paced
Learn at your own pace, whenever it's convenient for you. With no rigid schedule to worry about, you can take the course on your own terms.
03Community
Join a vibrant community of other students who are also learning with AI bootcamp 2. Ask questions, get feedback and collaborate with others to take your skills to the next level.
04Structured
Learn in a cohesive fashion that's easy to follow. With a clear progression from basic principles to advanced techniques, you'll grow stronger and more skilled with each module.
AI engineering in the enterprise
Master byte-level language models and advanced decoding strategies like top-k, nucleus, and speculative decoding
Design and optimize advanced Retrieval-Augmented Generation (RAG) pipelines that integrate text, images, tables, and structured data
Implement multi-vector indexing, late interaction methods, and metadata-aware query routing
Fine-tune retrievers and rerankers using contrastive loss, triplet loss, and hard-negative mining
Simulate and enhance reasoning in non-CoT models using feedback loops and model control patterns
Develop tool-using agents with multi-hop planning, disambiguation flows, and function-calling capabilities
Build feedback-driven evaluation pipelines using persona-based synthetic queries, regex/schema validators, and topic clustering
Learn and apply reinforcement learning techniques including DPO, PPO, GPRO, RLVR, and verifier-guided optimization
Integrate open-source frameworks like Hugging Face, DSPy, LangChain, OpenPipe, and Braintrust into production-grade systems
Deploy enterprise-ready AI systems with robust evaluation, monitoring, and continuous improvement loops
Architect modular, future-proof AI pipelines that can evolve with new models, tools, and retrieval methods
In this bootcamp, we go beyond prompt engineering to give you the skills to design, build, and optimize advanced AI systems that can adapt and improve over time. You will learn to think like a systems engineer, mastering the underlying mechanics of modern models and the techniques that make them perform in real-world, high-stakes environments.
Over several intensive weeks, we combine:
Deep technical instruction with hands-on coding projects to bridge the gap between theory and deployment. You’ll work directly with production-grade frameworks, simulate complex reasoning behaviors, and build AI pipelines that integrate multiple data types and modalities. Every concept is reinforced through practical exercises, live feedback, and real-world project reviews, ensuring that by the end, you can not only understand advanced AI architectures but also architect, deploy, and refine them for evolving enterprise needs. This program includes in-depth instruction, dedicated mentorship, and exclusive access to tools, templates, and a collaborative community to support your continued growth.
Your expert guides through this bootcamp are:
Dr. Dipen Bhuva: Dr. Dipen is an AI/ML researcher with 150+ citations and 16 published research papers. He has 3 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 Centre, Cleveland Clinic, and the US department of energy for his research papers. He was an official reviewer and has reviewed 100+ research papers from Elsevier, IEEE Transactions, ICRA, MDPI, and other top journals and conferences. He has a PhD from Cleveland State University with a focus on LLMs in cybersecurity. He also has a master's in informatics at Northeastern University.
Zao Yang: Zao is the owner of Newline, a platform used by 150k professionals from companies like Salesforce, Adobe, Disney, and Amazon. Zao has a rich history in the tech industry, co-creating Farmville (200 million users, $3B revenue) and Kaspa ($3B market cap). Self-taught in deep learning, generative AI, and machine learning, Zao is passionate about empowering others to develop practical AI applications. His extensive knowledge of both the technical and business sides of AI projects will be invaluable as you work on your own.
With Dipen and Zao's guidance, you’ll gain practical insights into building and deploying advanced AI models, preparing you for the most challenging and rewarding roles in the AI field.
AI engineering in the enterprise
Ability to architect and deploy advanced AI systems for enterprise and consulting, worth $100k in annual value ($1M over 10 years)
Skills in advanced RAG, RLHF, and RL-based fine-tuning that are rare and in high demand at top AI companies
Capability to build multimodal, feedback-driven AI pipelines that outperform vanilla retrieval and generation systems
Technical mastery to lead or consult on AI platform engineering, unlocking six-figure consulting opportunities
Portfolio of 50+ hands-on labs and multiple enterprise-grade AI projects to showcase to employers or clients
Direct code reviews, debugging help, and system design feedback from expert instructors
Future-proof understanding of AI system architecture, enabling you to adapt to new models and frameworks as they emerge
A competitive advantage in the AI job market, with potential to increase earnings by $50k+ annually
Our students work at
Bootcamp Syllabus and Content
Advanced RAG with Multi-Media RAG
1 Unit
- 01Advanced RAG with Multi-Media RAG
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Advanced RAG Reranker Training & Triplet Fundamentals
- Learn contrastive loss vs triplet loss approaches for training retrievers
- Understand tri-encoder vs cross-encoder performance trade-offs
- Master triplet-loss fundamentals and semi-hard negative mining strategies
- Fine-tune rerankers using Cohere Rerank API & SBERT (sbert.net, Hugging Face)
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Multimodal & Metadata RAG
- Index and query images, tables, and structured JSON using ColQwen-Omni (ColPali-based late interaction for audio, video, and visual documents)
- Implement metadata filtering, short vs long-term indices, and query routing logic
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Cartridges RAG Technique
- Learn how Cartridges compress large corpora into small, trainable KV-cache structures for efficient retrieval (~39x less memory, ~26x faster)
- Master the Self-Study training approach using synthetic Q&A and context distillation for generalized question answering
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Cartridge-Based Retrieval
- Learn modular retrieval systems with topic-specific "cartridges" for precision memory routing
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Late Interaction Methods
- Study architectures like ColQwen-Omni that combine multimodal (text, audio, image) retrieval using late interaction fusion
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Multi-Vector vs Single-Vector Retrieval
- Compare ColBERT/Turbopuffer vs FAISS, and understand trade-offs in granularity, accuracy, and inference cost
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Query Routing & Hybrid Memory Systems
- Explore dynamic routing between lexical, dense, and multimodal indexes
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Loss Functions for Retriever Training
- Compare contrastive loss vs triplet loss, and learn about semi-hard negative mining
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Reranker Tuning with SBERT or APIs
- Fine-tune rerankers (SBERT, Cohere API), evaluate with MRR/nDCG, and integrate into retrieval loops
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Exercises: Advanced RAG Techniques
- Implement triplet loss vs contrastive loss for reranker training with semi-hard negative mining
- Build multimodal RAG systems with images, tables, and query routing
- Compare single-vector (FAISS) vs multi-vector (ColBERT) retrieval
- Create cartridge-based RAG with topic-specific memory routing
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Advanced AI-Evals & Monitoring
1 Unit
- 01Advanced AI-Evals & Monitoring
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Advanced AI-Evals & Monitoring
- Scale LLM-judge for bulk multimodal outputs
- Build dashboards comparing judge accuracy vs IR metrics
- Implement auto-gate builds if accuracy drops below 95%
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Agent Failure Analysis Deep Dive
- Create transition-state heatmaps & tool states visualization
- Construct failure-matrices with LLM classification
- Develop systematic debugging workflows
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Enhancing RAG with Contextual Retrieval Recipes
- Use Instructor-driven synthetic data (Anthropic GitHub)
- Integrate web-search solutions (e.g., exa.ai)
- Apply LogFire, Braintrust augmentations
- Implement Cohere reranker + advanced logging
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Advanced Synthetic & Statistical Validation
- Generate persona-varied synthetic questions (angry/confused personas) and rewrite questions for better retrieval
- Perform embedding-diversity checks and JSONL corpus structuring
- Work with multi-vector databases
- Build parallel experimentation harness using ThreadPoolExecutor
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Strategic Feedback Collection
- Collect feedback with different types; use binary feedback (thumbs up/down) instead of stars
- Distinguish between two segment types: lack of data vs lack of capabilities
- Address common but fixable capability issues
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Dynamic Prompting & Validation
- Build dynamic UI with chain-of-thought wrapping using XML or streaming
- Incorporate validators with regex (e.g., checking fake emails generated by LLM)
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Data Segmentation & Prioritization
- Segment data based on patterns
- Apply Expected Value formula: Impact × Percentage of Queries × Probability of Success
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Topic Discovery with BERTopic
- Configure and apply BERTopic for unsupervised topic discovery
- Set up embedding model, UMAP, and HDBSCAN for effective clustering
- Visualize topic similarities and relationships
- Analyze satisfaction scores by topic to identify pain points
- Create matrices showing relationship between topics and satisfaction
- Identify the "danger zone" of high-volume, low-satisfaction query areas
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Persona-Driven Synthetic Queries
- Generate diverse queries (angry, curious, confused users) to stress-test retrieval and summarization pipelines
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Regex & Schema Validators for LLM Outputs
- Add lightweight automated checks for emails, JSON formats, and other structural expectations
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Segmentation-Driven Summarization
- Build summarization-specific chunks, integrate financial metadata, and compare with BM25 retrieval
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Failure-Type Segmentation
- Classify failures into retrieval vs generation errors to guide improvement priorities
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Clustering Queries with BERTopic
- Use UMAP + HDBSCAN to group user queries into semantically meaningful clusters
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Mapping Feedback to Topics
- Overlay evaluator scores onto clusters to identify weak performance areas
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Danger Zone Heatmaps
- Visualize query volume vs success rates to prioritize high-impact fixes
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Feedback-to-Reranker Loop
- Build iterative reranking systems driven by topic segmentation and evaluation feedback
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Dynamic Prompting for Tool Selection
- Teach LLMs to output structured tool calls reliably (JSON schema, guardrails, few-shots)
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Tool Disambiguation and Clarification Loops
- Design prompts that force models to ask clarifying questions before executing
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XML-Based CoT Streaming for Agents
- Output reasoning traces in structured XML-like format for real-time dashboards or UIs
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Production-Grade Project
- Deploy a full RAG + fine-tuned LLM service
- Add multiple tools with RAG and implement tool routing
- Include multimodal retrieval, function-calling, LLM-judge pipeline, and monitoring
- Achieve ≥ 95% end-to-end task accuracy
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Exercises: AI Evaluation & Monitoring Pipeline
- Build LLM-as-judge evaluation pipelines with accuracy dashboarding
- Apply BERTopic for failure analysis and danger zone heatmaps
- Generate persona-driven synthetic queries for stress-testing
- Implement automated quality gates with statistical validation
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Intro RL & RLHF
1 Unit
- 01Intro RL & RLHF
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Markov Processes as LLM Analogies
- Frame token generation as a Markov Decision Process (MDP) with states, actions, and rewards
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Monte Carlo vs Temporal Difference Learning
- Compare Monte Carlo episode-based learning with Temporal Difference updates, and their relevance to token-level prediction
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Q-Learning & Policy Gradients
- Explore conceptual foundations of Q-learning and policy gradients as the basis of RLHF and preference optimization
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RL in Decoding and Chain-of-Thought
- Apply RL ideas during inference without retraining, including CoT prompting with reward feedback and speculative decoding verification
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Exercises: RL Foundations with Neural Networks
- Implement token generation as MDP with policy and value networks
- Compare Monte Carlo vs Temporal Difference learning for value estimation
- Build Q-Learning from tables to DQN with experience replay
- Implement REINFORCE with baseline subtraction and entropy regularization
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RL & RLHF Framework
1 Unit
- 01RL & RLHF Framework
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DSPy + RL Integration
- Explore DSPy's prompt optimizer and RL system built into the pipeline
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LangChain RL
- Use LangChain's experimental RL chain for reinforcement learning tasks
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RL Fine-Tuning with OpenAI API
- Implement RL fine-tuning using OpenAI's API
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RL Fine-Tuning Applications
- Apply RL fine-tuning for state-of-the-art email generation
- Apply RL fine-tuning for summarization tasks
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RL Fine-Tuning with OpenPipe
- Use OpenPipe for RL fine-tuning workflows
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DPO/PPO/GPRO Comparison
- Compare Direct Preference Optimization, Proximal Policy Optimization, and GPRO approaches
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Reinforcement Learning with Verifiable Rewards (RLVR)
- Learn about RLVR methodology for training with verifiable reward signals
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Rubric-Based RL Systems
- Explore rubric-based systems to guide RL at inference time for multi-step reasoning
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Training Agents to Control Web Browsers
- Train agents to control web browsers with RL and Imitation Learning
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Exercises: RL Frameworks & Advanced Algorithms
- Compare DSPy vs LangChain for building QA systems
- Implement GRPO and RLVR algorithms
- Build multi-turn agents with turn-level credit assignment
- Create privacy-preserving multi-model systems (PAPILLON) with utility-privacy tradeoffs
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How RAG Finetuning and RLHF Fits in Production
1 Unit
- 01How RAG Finetuning and RLHF Fits in Production
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End-to-End LLM Finetuning & Orchestration using RL
- Prepare instruction-tuning datasets (synthetic + human)
- Finetune a small LLM on your RAG tasks
- Use RL to finetune the same dataset and compare results across all approaches
- Select the appropriate finetuning approach and build RAG
- Implement orchestration patterns (pipelines, agents)
- Set up continuous monitoring integration using Braintrust
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RL Frameworks in Practice
- Use DSPy, OpenAI API, LangChain's RLChain, OpenPipe ART, and PufferLib for RLHF tasks
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Rubric-Based Reward Systems
- Design interpretable rubrics to score reasoning, structure, and correctness
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Real-World Applications of RLHF
- Explore applications in summarization, email tuning, and web agent fine-tuning
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RL and RLHF for RAG
- Apply RL techniques to optimize retrieval and generation in RAG pipelines
- Use RLHF to improve response quality based on user feedback and preferences
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Exercises: End-to-End RAG with Finetuning & RLHF
- Finetune a small LLM (Llama 3.2 3B or Qwen 2.5 3B) on ELI5 dataset using LoRA/QLoRA
- Apply RLHF with rubric-based rewards to optimize responses
- Build production RAG with DSPy orchestration, logging, and monitoring
- Compare base → finetuned → RLHF-optimized models
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AI bootcamp 2
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Frequently Asked Questions
How is this different from other AI bootcamps?
Most AI bootcamps focus on either beginner programming with AI APIs or narrow workflows like basic RAG or fine-tuning. Bootcamp 2 is designed for engineers who want to master advanced AI systems engineering — including byte-level modeling, multi-vector and multimodal RAG, reinforcement learning (DPO, PPO, RLVR), verifier-guided pipelines, and tool-using agents. It combines deep technical theory with 50+ hands-on labs to build production-ready systems. To my knowledge, few programs cover advanced RAG and RLHF together at this depth, especially with a focus on evaluation, feedback loops, and enterprise deployment.
What should I look for in this AI Bootcamp?
Bootcamp 2 is a project-focused program where you learn by building advanced AI pipelines from scratch. It’s built for people who want to go beyond prompt engineering and API wrappers, and gain mastery in byte-level models, reasoning simulation, advanced retrieval architectures, and RL-based fine-tuning. You’ll work on multimodal RAG, feedback-driven rerankers, and RLHF-ready agents — with direct guidance from expert instructors who have shipped large-scale AI products.
Who is this Artificial Intelligence Bootcamp ideal for?
This bootcamp is ideal for engineers, data scientists, and technical founders who want to build enterprise-ready AI systems. Whether you’re implementing advanced RAG for private, on-premise data, fine-tuning models with smaller datasets, or applying RLHF techniques to AI agents, this program equips you with the skills and frameworks needed to operate at a senior level.
Are there any required skills or Python programming experience needed before enrolling?
You must have basic Python programming and debugging skills. This is not a beginner’s AI course — it assumes you can follow code, run experiments, and troubleshoot errors. Prior completion of Bootcamp 1 or equivalent experience is recommended.
What is the course structure?
Weekly commitment is approximately 3 hours for lectures and office hours, plus 2–4 hours of hands-on coding. The program runs for multiple weeks, with 50+ labs and mini-projects, culminating in enterprise-ready systems you can deploy. All sessions are recorded, and live schedules are designed to accommodate different time zones.
Do I need any pre-requisite?
You need to be comfortable programming in Python and committed to completing advanced coding projects. We assume you have completed Bootcamp 1 or have equivalent knowledge of LLM fundamentals.
Anything I need to prepare?
It’s best to come with a project idea — for example, a multimodal RAG system for your company data or an RLHF-tuned assistant for a niche use case. The curriculum is flexible enough to adapt your learning to your goals.
Why should I take the Artificial Intelligence Bootcamp from newline?
Bootcamp 2 is uniquely focused on advanced RAG, reinforcement learning, and RLHF. You’ll learn to design and debug full-stack AI systems with evaluation and feedback loops, not just call APIs. You also get mentorship from practitioners who have shipped large-scale AI systems and can help you adapt the techniques to your own projects.
To what extent will the program delve into generative AI concepts and applications?
This bootcamp goes far beyond generative AI basics. You’ll explore advanced model architectures, reasoning control, retrieval fusion across modalities, RL-based adaptation, and multi-hop agent planning. Everything is taught through runnable code that you can adapt to real-world applications.
What are the career outcomes after completing the AI Bootcamp with newline?
Graduates are equipped for roles such as senior AI engineer, AI systems architect, enterprise AI consultant, and startup founder in the AI space. You will have the skills to build, deploy, and optimize advanced AI systems for enterprise-grade use cases.
Are there any hands-on projects incorporated into the AI Bootcamp curriculum?
Yes. You will complete over 50 labs and multiple large-scale projects, including a full multimodal RAG pipeline, feedback-driven reranker tuning, and RLHF-ready agent deployment. Every project is designed to be directly applicable to real-world scenarios.
Do you have a guarantee?
Yes. If you commit to the work, we guarantee you will complete a project that meets your goals. We will align on project scope, budget, and time commitment upfront, and provide ongoing guidance to ensure you can ship a functional, well-evaluated system.
What is the target audience?
There are three core groups: engineers applying advanced RAG and RL fine-tuning for private data; builders creating vertical foundational models on smaller datasets; and technical professionals leveraging these skills for consulting or AI startup ventures.
Will you be covering multi-modal applications?
Yes. You will build retrieval systems and agents that handle text, images, tables, audio, and structured data — integrating them into unified, query-routed pipelines.
Book a call with us
Want to learn more about the bootcamp and how it can help advance your career? Schedule a free consultation call with our team.
Book a call
