Type: Web Article Original link: https://verdik.substack.com/p/how-to-get-consistent-classification Publication date: 2025-10-23
Author: Verdi
Summary #
WHAT - This article describes a technique to obtain consistent classifications from large language models (LLM) that are inherently stochastic. The author presents a method to determine consistent labels using vector embeddings and vector search, with an implementation benchmarked in Golang.
WHY - It is relevant for AI business because it addresses the problem of label variability generated by LLMs, improving consistency and efficiency in classifying large volumes of unlabeled data.
WHO - The author is Verdi, a machine learning expert. The main actors include ML developers, companies using LLMs for data labeling, and the AI research community.
WHERE - It positions itself in the market of AI solutions for data labeling, offering an alternative method to the APIs of major model providers.
WHEN - The technique is current and responds to an emerging need in the context of the widespread use of LLMs for data labeling. The maturity of the solution is demonstrated through benchmarks and practical implementations.
BUSINESS IMPACT:
- Opportunities: Implementing this technique can reduce costs and improve consistency in data labeling, making the process of training machine learning models more efficient.
- Risks: Dependence on third-party APIs for labeling could be mitigated, but investment in infrastructure for managing vector embeddings is required.
- Integration: The technique can be integrated into the existing stack using Pinecone for vector search and embeddings generated by models such as GPT-3.5.
TECHNICAL SUMMARY:
- Core technology stack: Golang for implementation, GPT-3.5 for label generation, voyage-.-lite for embedding (dimension 768), Pinecone for vector search.
- Scalability and architectural limits: The solution is scalable but requires computational resources for managing vector embeddings and vector search. The main limitations are related to initial latency and setup costs.
- Key technical differentiators: Use of vector embeddings to cluster inconsistent labels, vector search to find similar labels, and path compression to ensure label consistency.
Use Cases #
- Private AI Stack: Integration into proprietary pipelines
- Client Solutions: Implementation for client projects
- Strategic Intelligence: Input for technological roadmap
- Competitive Analysis: Monitoring AI ecosystem
Resources #
Original Links #
- How to Get Consistent Classification From Inconsistent 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 13:57 Original source: https://verdik.substack.com/p/how-to-get-consistent-classification
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