Type: Hacker News Discussion Original link: https://news.ycombinator.com/item?id=45571423 Publication date: 2025-10-13
Author: frenchmajesty
Summary #
WHAT - Techniques for obtaining consistent classifications from stochastic large language models (LLM) with implementation in Golang. Solves the problem of inconsistency in labels generated by models.
WHY - Relevant for improving the reliability of automated classifications, reducing errors and costs associated with manual labeling. Solves the problem of inconsistency in labels generated by models.
WHO - Author: Verdi Oct. Community of developers and ML engineers, users of language model APIs.
WHERE - Positioned in the market of AI solutions for automated labeling, aimed at development teams and companies using LLMs.
WHEN - New approach, emerging trend. The discussion on Hacker News indicates current interest and potential adoption.
BUSINESS IMPACT:
- Opportunities: Improvement in data label quality, reduction of operational costs, increase in efficiency in data labeling processes.
- Risks: Dependence on external APIs, potential technological obsolescence.
- Integration: Possible integration with existing stack for automated labeling, improvement of data labeling workflows.
TECHNICAL SUMMARY:
- Core technology stack: Golang, language model APIs (e.g., OpenAI), logit_bias, json_schema.
- Scalability: Good scalability thanks to the use of external APIs, limits related to the management of large volumes of data.
- Technical differentiators: Use of logit_bias and json_schema to improve label consistency, implementation in Golang for high performance.
HACKER NEWS DISCUSSION: The discussion on Hacker News mainly highlighted issues related to performance and technical problem-solving. Users discussed the challenges related to the implementation of automated labeling solutions and potential technical solutions. The general sentiment is one of interest and curiosity, with some caution regarding dependence on external APIs. The main themes that emerged were performance, technical problems, and database management. The community showed a practical and technical interest, with a focus on solving concrete problems related to the use of LLMs.
Use Cases #
- Private AI Stack: Integration in proprietary pipelines
- Client Solutions: Implementation for client projects
Third-Party Feedback #
Community feedback: The HackerNews community commented with a focus on performance, problem (20 comments).
Resources #
Original Links #
- My trick for getting consistent classification from LLMs - Original link
Article recommended and selected by the Human Technology eXcellence team, processed through artificial intelligence (in this case with LLM HTX-EU-Mistral3.1Small) on 2025-10-23 13:56 Original source: https://news.ycombinator.com/item?id=45571423
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