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Hamel Husain

Hamel Husain

@hamel-husain · 7 positions · On ppll ↗

Machine learning engineer and independent AI consultant; writes about LLM evaluation and tooling at hamel.dev.

Hamel Husain did not write this page.

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  1. software quality

    Even with perfect models, you still need to verify they're solving the right problem. The need for systematic error analysis, domain-specific testing, and monitoring will still be important.

    ↗AI Evals: Everything You Need to Knowhamel.dev 1st of 2 in this piece

  2. LLMs

    Additionally, a LLM cannot read your mind, and research shows that people need to observe the LLM's behavior in order to properly externalize their requirements.

    ↗AI Evals: Everything You Need to Knowhamel.dev 2nd of 2 in this piece

  3. 3 months earlier
  4. software quality

    Artifacts that are hard for you to verify are often hard for users too.

    ↗“It’s Hard to Eval” Is a Product Smellhamel.dev 1st of 3 in this piece

  5. designsoftware quality

    More importantly, designing your product for ease of verification should come before building evals.

    ↗“It’s Hard to Eval” Is a Product Smellhamel.dev 2nd of 3 in this piece

  6. software quality

    Before AI, verification often happened incidentally during the process of creating work product. With AI, verification is the bottleneck.

    ↗“It’s Hard to Eval” Is a Product Smellhamel.dev 3rd of 3 in this piece

  7. 5 months earlier
  8. Notebooks don't force you to write clean code. AI tools don't force you to think carefully. Discipline comes from the developer, not the environment.

    ↗Why I Stopped Using nbdevhamel.dev

  9. documentation

    This reinforced my belief that good documentation comes from effort, not tooling.

    ↗Why I Stopped Using nbdevhamel.dev