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rss_feedHamel Husain ·15.08.2025 open_in_new

Q: Should I outsource annotation & labeling to a third party? – Hamel’s Blog - Hamel Husain

Outsourcing error analysis is usually a big mistake (with some exceptions). The core of evaluation is building the product intuition that only comes from systematically analyzing your system’s failures. You should be extremely skeptical of this process being delegated.

The Dangers of Outsourcing

When you outsource annotation, you often break the feedback loop between observing a failure and understanding how to improve the product. Problems with outsourcing include:

  • Superficial Labeling: Even well-defined metrics require nuanced judgment that external teams lack. A critical misstep in error analysis is excluding domain experts from the labeling process. Outsourcing this task to those without domain expertise, like general developers or IT staff, often leads to superficial or incorrect labeling.
  • Loss of Unspoken Knowledge: A principal domain expert possesses tacit knowledge and user understanding that cannot be fully captured in a rubric. Involving these experts helps uncover their preferences and expectations, which they might not be able to fully articulate upfront.
  • Annotation Conflicts and Misalignment: Without a shared context, external annotators can create more disagreement than they resolve. Achieving alignment is a challenge even for internal teams, which means you will spend even more time on this process.

    • The Recommended Approach: Build Internal Capability

    Instead of outsourcing, focus on building an efficient internal evaluation process.

    1. Appoint a “Benevolent Dictator”. For most teams, the most effective strategy is to appoint a single, internal domain expert as the final decision-maker on quality. This individual sets the standard, ensures consistency, and develops a sense of ownership.

    2. Use a collaborative workflow for multiple annotators. If multiple annotators are necessary, follow a structured process to ensure alignment: * Draft an initial rubric with clear Pass/Fail definitions and examples. * Have each annotator label a shared set of traces independently to surface differences in interpretation. * Measure Inter-Annotator Agreement (IAA) using a chance-corrected metric like Cohen’s Kappa. * Facilitate alignment sessions to discuss disagreements and refine the rubric. * Iterate on this process until agreement is consistently high.

    How to Handle Capacity Constraints

    Building internal capacity does not mean you have to label every trace. Use these strategies to manage the workload:

  • Smart Sampling: Review a small, representative sample of traces thoroughly. It is more effective to analyze 100 diverse traces to find patterns than to superficially label thousands.
  • The “Think-Aloud” Protocol: To make the most of limited expert time, use this technique from usability testing. Ask an expert to verbalize their thought process while reviewing a handful of traces. This method can uncover deep insights in a single one-hour session.
  • Build Lightweight Custom Tools: Build custom annotation tools to streamline the review process, increasing throughput.

    • Exceptions for External Help

    While outsourcing the core error analysis process is not recommended, there are some scenarios where external help is appropriate:

  • Purely Mechanical Tasks: For highly objective, unambiguous tasks like identifying a phone number or validating an email address, external annotators can be used after a rigorous internal process has defined the rubric.
  • Tasks Without Product Context: Well-defined tasks that don’t require understanding your product’s specific requirements can be outsourced. Translation is a good example: it requires linguistic expertise but not deep product knowledge.
  • Engaging Subject Matter Experts: Hiring external SMEs to act as your internal domain experts is not outsourcing; it is bringing the necessary expertise into your evaluation process. For example, AnkiHub hired 4th-year medical students to evaluate their RAG systems for medical content rather than outsourcing to generic annotators.

    • ↩︎ Back to main FAQ

    This article is part of our AI Evals FAQ, a collection of common questions (and answers) about LLM evaluation. View all FAQs or return to the homepage.