How LLMs Work Inside: Tokens, Attention and Sampling
LLM Internals: leave with a notebook exploring tokenization, attention weights and temperature/top-p effects.
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Learn this topic with a mentor
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- Bring your own work — code, campaign, report or plan
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What this session would cover
Proposed outline — the mentor finalises the agenda once this topic is scheduled.
- 1Why llm internals matters — the common problem: LLM quirks like token limits and random outputs confuse developers building on them.
- 2Core concepts in plain language: Tokens, tokenization, embeddings, positional representations, self-attention, encoder and decoder architectures
- 3Going further: context windows, pretraining objectives, next-token prediction, mixture-of-experts models, decoding, sampling
- 4Framework walkthrough: Transformer Architecture, Prompt → Retrieve → Generate → Verify, Evaluation Sets
- 5Practical workflow, built live: A notebook exploring tokenization, attention weights and temperature/top-p effects.
- 6How to measure it: Groundedness, Answer accuracy on an eval set, Latency, Cost per request
- 7An illustrative case (a fictional example, not a client result), then live Q&A on your own situation
Who it's for
- • Students and freshers entering tech
- • Working developers and engineers
- • Tech leads and architects
You'd leave with
- A notebook exploring tokenization, attention weights and temperature/top-p effects.
- A working understanding of Transformer Architecture and Prompt → Retrieve → Generate → Verify
- A short list of measures to track: Groundedness, Answer accuracy on an eval set, Latency
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