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How to learn how LLMs generate text one token at a time with LLM Inference Engineering

Work through the Part 1 basics on autoregressive generation and the prefill and decode phases.

  • Top pick
  • Research
  • Beginner
  • 30 minutes
  • From the README
  • Any agent

Who it is forA developer new to LLM serving who needs the core generation loop before reading about optimizations.

The problemTerms like prefill, decode, and autoregressive appear everywhere in inference docs, but they are hard to follow without the underlying loop.

The outcomeA clear mental model of the generation loop, the two inference phases, and the metrics TTFT and TPOT.

What you need first

  • A web browser to open the linked blog posts
  • Basic familiarity with how text generation works, or willingness to start with the Part 1 basics

Steps

  1. Open the Autoregressive Models post at https://outcomeschool.com/blog/autoregressive-models and read the generation loop and the numeric example.
  2. Open the Prefill vs Decode post at https://outcomeschool.com/blog/prefill-vs-decode-llm-inference-optimization and read the comparison table.
  3. Read the key metrics section on TTFT, TPOT, throughput, and end-to-end latency in the same post.
  4. Write a short summary of the difference between prefill and decode in your own words.

Prompt for your agent

Using the amitshekhariitbhu/llm-inference-engineering repo, read the Part 1 section on <topic> and produce a one-page summary of its key terms. Verify that each term in the summary is defined in the linked Outcome School post.

Caveat. The repo is a reading series that links to external blog posts; the README itself contains no code or runnable setup.

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