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AiTechWorlds
AiTechWorlds
An advanced, comprehensive roadmap for those who want to deeply understand, contribute to, and advance the field of large language models — covering math, classical ML, deep learning, transformer architecture, fine-tuning, alignment, RAG, and research writing.
Large language models represent the most transformative technology of the decade. Understanding them deeply — not just using them but being able to extend, improve, and critique them — requires a genuine investment in mathematical and computational foundations. This roadmap is designed for those who want to go beyond being an LLM user and become an LLM researcher or research engineer.
| Paper | Year | Contribution | Must-Read Priority |
|---|---|---|---|
| Attention Is All You Need (Vaswani et al.) | 2017 | Transformer architecture | Essential |
| BERT: Pre-training of Deep Bidirectional Transformers | 2018 | Masked language modelling | Essential |
| Language Models are Few-Shot Learners (GPT-3) | 2020 | In-context learning at scale | Essential |
| Training language models to follow instructions (InstructGPT) | 2022 | RLHF alignment methodology | Essential |
| LLaMA: Open and Efficient Foundation Language Models | 2023 | Open-weight large models | Essential |
| LoRA: Low-Rank Adaptation of LLMs | 2021 | Efficient fine-tuning | Essential |
| Retrieval-Augmented Generation for NLP | 2020 | RAG architecture | Essential |
| Constitutional AI: Harmlessness from AI Feedback | 2022 | RLAIF alignment | Important |
| Scaling Laws for Neural Language Models | 2020 | Model scaling theory | Important |
| The Llama 3 Herd of Models | 2024 | Modern open LLM training | Important |
Efficiency and Compression:
Alignment and Safety:
Capabilities Research:
Evaluation:
Quite a lot — and the honest answer is more than most bootcamps prepare you for. Linear algebra (matrix operations, eigendecomposition), calculus (automatic differentiation, gradient flows), probability theory (distributions, expectations, KL divergence), and information theory (entropy, mutual information) are all actively used. The good news: you do not need to master all of this before starting. Study the math alongside the ML topics, returning to deepen the theory as you encounter it in practice.
LLM researchers focus on advancing the science: proposing new architectures, training methods, alignment techniques, or evaluation frameworks, and publishing findings in peer-reviewed venues. LLM engineers focus on building practical systems: fine-tuning models for specific tasks, building RAG pipelines, deploying models at scale, and optimising inference. Many roles at AI companies blend both. This roadmap covers the researcher trajectory but the skills are directly applicable to advanced engineering roles.
Increasingly no. The field moves too fast for traditional academic timelines to be the only path. Many of the most cited papers come from engineers at AI labs (OpenAI, Anthropic, Google DeepMind, Meta AI) who do not hold PhDs. Strong open source contributions, reproducible research, and publicly available work (arXiv preprints, GitHub repos with implementations) carry significant weight. A PhD provides depth, mentorship, and academic network access — but self-directed researchers who demonstrate genuine contributions are taken seriously.
Prioritise PyTorch proficiency and transformer implementation over breadth. Being able to implement a transformer from scratch, train it on a small dataset, debug training instabilities, and measure performance rigorously is the core skill. Everything else — fine-tuning, RLHF, RAG — is built on top of this foundation. Supplement with deliberate paper reading: aim to read and genuinely understand two significant papers per week.
Follow these steps in order. Required steps are marked — optional steps accelerate your learning.
Linear algebra (matrices, eigenvalues, SVD), probability and statistics (Bayes theorem, MLE, distributions), calculus (gradients, chain rule, Jacobians), and information theory (entropy, KL divergence).
Regression, classification, clustering, SVMs, decision trees, gradient boosting, model evaluation, and statistical learning theory. Build intuition before diving into deep learning.
Backpropagation, CNNs, RNNs and LSTMs, batch normalisation, dropout, learning rate schedules, and training stability. Implement models from scratch in PyTorch.
Understand scaled dot-product attention, multi-head attention, positional encodings, encoder vs decoder vs encoder-decoder architectures, and modern variants (Flash Attention, RoPE, ALiBi).
Study the original papers and architectural decisions behind GPT-2/3/4, BERT, T5, LLaMA 1/2/3, and Mistral. Understand tokenisation (BPE, SentencePiece), pre-training objectives, and scaling laws.
Understand when and why to fine-tune, implement LoRA and QLoRA for parameter-efficient fine-tuning on consumer hardware, and understand PEFT libraries (Hugging Face PEFT, Unsloth).
Study Reinforcement Learning from Human Feedback: reward model training, PPO optimisation, DPO (Direct Preference Optimisation), and Constitutional AI. Understand the alignment tax and Goodhart's Law.
Build production RAG systems: embedding models, vector databases, retrieval strategies (dense, sparse, hybrid), reranking, and evaluation metrics (faithfulness, relevance, groundedness).
Understand vision-language models (CLIP, LLaVA, GPT-4V), image tokenisation approaches, cross-modal attention, and the challenges of aligning different modalities.
Develop a systematic approach to reading research papers: three-pass method, understanding experimental design, critically evaluating claims, and maintaining a personal research database.
Learn to write research papers in LaTeX, structure an experiment, write clear and honest results sections, navigate the peer review process, and submit to venues like NeurIPS, ICML, and ACL.
Make a meaningful contribution to a major open source AI project (Hugging Face Transformers, LlamaIndex, vLLM, Axolotl). This demonstrates research engineering ability and builds your reputation in the community.
Ready to start your journey?
Begin with the first step. Consistency beats intensity — just 30 minutes a day.