AI360Xpert
Learning Paths

LLM Engineering

Engineer a large language model end to end — from raw tokens through pretraining, then branch into retrieval and fine-tuning before both tracks merge into a single served, evaluated system.

  1. Foundations

    1. Tokenization

      Split raw text into the discrete tokens a model actually consumes. Learn byte-pair and subword encoding, vocabulary-size trade-offs, and why token boundaries shape everything downstream — from context limits to inference cost.

    2. Embeddings

      Map tokens into a continuous vector space where semantic similarity becomes geometry. Understand embedding tables, positional information, and how dense representations let a model reason over meaning rather than surface strings.

    3. Transformer Architecture

      Assemble self-attention, feed-forward blocks, and residual connections into the layered stack behind modern LLMs. See how attention lets every token weigh every other, and why the design parallelizes so well on accelerators.

    4. Pretraining

      Train the base model on a vast corpus of raw tokens with a next-token objective. This is where broad capability emerges — and the point at which the path forks toward two complementary ways to make the model genuinely useful.

  2. Specialization Tracks

    1. Retrieval

      Retrieval-Augmented Generation

      Ground responses in an external knowledge base: embed documents, retrieve the most relevant passages at query time, and condition generation on them. Cuts hallucination and keeps answers current with no retraining.

    2. Fine-tuning

      Fine-tuning & Alignment

      Adapt the base model to tasks and preferences with supervised fine-tuning and alignment methods such as RLHF or DPO. Teaches format, tone, and safety while preserving the broad knowledge learned during pretraining.

  3. Production

    1. Serving & Evaluation

      Both tracks rejoin here. Stand up low-latency inference — batching, KV-caching, quantization — behind an API, and measure quality with automated evals and human review before anything reaches users.

    2. Deployment & Monitoring

      Ship the evaluated model to production and keep it healthy: track latency, cost, drift, and safety signals, then close the loop by feeding real-world failures back into retrieval and fine-tuning.