Step-by-Step LLM Engineering Projects Roadmap (2026 Edition)

X postAhmadResource

Ahmad (@TheAhmadOsman) shares a comprehensive, project-based roadmap for LLM engineering. The guide advocates learning by building the entire LLM stack from scratch—from tokenizers and attention mechanisms to KV cache, serving, and evaluation.

Key points

  1. The core learning loop: Build the primitive from scratch, plot metrics (loss, latency, memory curves), break it intentionally via ablations, explain the failure, and ship the artifact.
  2. Foundational mechanics: Tokenization (BPE, SentencePiece), embeddings, positional methods (sinusoidal, RoPE, ALiBi), scaled dot-product attention, and Transformer blocks.
  3. Inference and optimization techniques: KV cache variants (MQA, GQA, MLA), speculative decoding, long-context handling, and hardware-aware modeling.
  4. Systems and training layer: Mixture of Experts (MoE), state-space models, data pipelines, post-training (RLHF, PPO, GRPO, RLVR), quantization, serving runtimes, RAG, and agents.
  5. Built on a four-part foundation series covering GPU memory math, memory bandwidth, inference engines, and token-by-token mechanics.

Why it matters

As off-the-shelf AI tooling matures, deep technical value shifts toward understanding underlying system dynamics. Hands-on reproduction of architectural primitives demystifies memory bottlenecks, decoding limits, and serving trade-offs.

Analysis

Ahmad presents an engineering-first pedagogical path that rejects passive reading in favour of empirical testing and stress testing. Intentionally breaking components provides practical intuition that high-level abstractions hide. While community commentators debate whether mastering these topics requires six months, one year, or two years, the curriculum outlines a rigorous blueprint for moving from consumer to systems engineer.

Original post

Ahmad

@TheAhmadOsman · 22 August 2026

@RoryCrave This should do t.co/WwlQsQap7P

288 likes3 replies975 bookmarks18.5k views

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Selected replies (5)
  • @DavidOndrej1 @TheAhmadOsman even less
  • @KshitijxFRL @TheAhmadOsman 24 months is a very long time, it can be done in way less time.
  • @navaneethvb @TheAhmadOsman I don't think u need 2 years, honestly, max 1 year
  • @DhairyasheelPa5 @TheAhmadOsman I require 6 months
  • @Mayoandham @TheAhmadOsman Been in it 9 months and think I am 95% more knowledgeable than most people. Ive meet 1 other person who I met face to face that we had a decent conversation. Most people think I am talking about gpt..not to mention graph engineering, loops and orchestration

Summary generated by AI (Gemini 3.8 Flash, 27 September 2026). It may contain errors – the original source is authoritative.

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