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In the last chapter (Part 12), we explored how language models can be adapted via fine-tuning.
We began by discussing the central question of when fine-tuning is actually worth doing. We studied the reasons to fine-tune and the reasons to avoid it.
After that, we moved to understanding PEFT techniques. In particular, we explored LoRA and QLoRA. We understood...
In the last chapter (Part 11), we completed our journey of understanding how evaluation works in LLM systems.
We began by exploring multi-turn evaluation. Unlike single-turn scenarios where one prompt produces one response, conversational systems require evaluating behavior across an entire dialogue. A response in later turns often depends on things that happened ...
In part 10, we expanded our understanding of evaluation by exploring model benchmarks and the evaluation of LLM-powered applications within their real operational context.
We began by exploring model capability benchmarks. We understood that these benchmarks attempt to measure the general intelligence, reasoning ability, and knowledge breadth of large language mod...
In Part 9, we began exploring the evaluation space of LLM applications, laying the groundwork, covering challenges and a practical taxonomy of evaluation methods.
We began by examining why LLM evaluation is different from traditional software or classical ML evaluation. Unlike deterministic systems, LLMs generate open-ended, probabilistic outputs. This introduces ...