Build Your Own LLM from Scratch: The 5 Essential Stages Behind GPT and Claude

Misconception about architecture : Many assume that the Transformer architecture is the core secret of models like Claude, but the article argues that architecture is the least important factor; data, evaluation, and engineering systems decide a model’s success.

01 – Common misconceptions : The author emphasizes that the real breakthrough lies in engineering the model, not merely inventing a new Transformer.

02 – Pre‑training – learning language : The goal is next‑token prediction using massive text corpora. Text is tokenized with Byte‑Pair Encoding (BPE), which influences all downstream steps.

03 – Data – the decisive factor : Data collection starts with Common Crawl (hundreds of petabytes, 2500 billion pages). The raw data is low‑quality and must undergo a strict multi‑step filtering pipeline:

Extract text from HTML, handling formulas and templates.

Filter harmful content (violence, pornography, personal data).

Deduplicate by URL, document, and sentence, removing repeated headers/footers.

Heuristic filtering based on token count, anomalous tokens, and low‑quality documents.

Model‑based filtering: predict whether a page could be cited by Wikipedia.

Data balancing: categorize data (code, books, entertainment, etc.) and adjust domain weights according to scaling laws.

The author stresses that data quality outweighs quantity; closed‑source datasets are far larger (e.g., LLaMA 3 trained on 15 trillion tokens, GPT‑4 estimated at 13 trillion).

04 – Scaling laws – optimal compute allocation : Given a fixed GPU budget (e.g., 10 000 GPUs for one month), the scaling law tells whether to train a larger model or use more data. Empirically, more data and larger models improve performance, and one can predict outcomes before training. Modern pipelines first tune hyper‑parameters on a small model, extrapolate along the scaling curve, then run a single large‑scale training.

The Chinchilla finding that each parameter should see about 20 training tokens is cited as the compute‑optimal ratio, but for inference‑cost‑aware training the ratio rises to >150 tokens per parameter.

05 – Post‑training – turning a predictor into an assistant : After pre‑training, the model can only continue text, not engage in dialogue. Supervised fine‑tuning (SFT) uses a few thousand prompt + high‑quality answer pairs to clone behavior (e.g., Alpaca fine‑tuned LLaMA 7B with 52 000 generated instructions). However, SFT suffers from limited human capacity, hallucinations, and high annotation cost.

Reinforcement Learning from Human Feedback (RLHF) addresses these issues by collecting preference data: humans choose the better of two model outputs, a reward model is trained on these preferences, and PPO optimizes the LLM to maximize the reward. Direct Preference Optimization (DPO) is presented as a simpler, supervised alternative that achieves comparable results.

06 – Evaluation and engineering systems :

Evaluation : During pre‑training perplexity measures token‑level uncertainty (70 → <10 from 2017‑2023). After alignment, benchmarks replace perplexity: MMLU, HELM, robot‑arena Elo rankings, and AlpacaEval (≈98 % correlation with robot‑arena, < $10 cost, < 3 min runtime). Scores can vary widely with prompt format.

Engineering : GPU memory limits (e.g., 7 B‑parameter model needs ~112 GB). Optimizations include:

Half‑precision (bf16) to halve memory and speed up.

Operator fusion and chunking; FlashAttention yields ~1.7× end‑to‑end speedup.

Data parallelism with ZeRO optimizer state sharding.

Model parallelism (pipeline or tensor slicing).

Sparsity via Mixture‑of‑Experts (MoE) – more parameters but constant compute per token.

07 – Core logic recap : Reviewing the five stages shows that architecture occupies only a small slice; the decisive components are data, scaling, alignment, evaluation, and engineering.

08 – Common pitfalls :

Focusing on architecture tweaks – low‑impact.

Treating data as a generic resource – low‑quality data caps model performance.

Ignoring Chinchilla scaling – mismatched model‑data ratios waste compute.

Stopping after SFT – without RLHF/DPO the model won’t align with human preferences.

Continuing to use perplexity after alignment – no longer a reliable metric.

09 – Conclusion : Top‑tier LLMs are not merely “trained”; they are engineered. While many obsess over the Transformer, the real differentiators are high‑quality data pipelines, optimal scaling, human‑aligned fine‑tuning, rigorous evaluation, and efficient compute infrastructure.