# facets-of-likelihood

Published articles for facets-of-likelihood.

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## Why Your LLM Keeps Matching the Wrong Products (And Why a Better Prompt Won't Fix It)

DevFeed: [Why Your LLM Keeps Matching the Wrong Products (And Why a Better Prompt Won't Fix It)](<https://devfeed.tech/articles/why-your-llm-keeps-matching-the-wrong-products-and-why-a-better-prompt-won-t-fix-it-114862.md>)

Original publisher: [Read original article](<https://hackernoon.com/why-your-llm-keeps-matching-the-wrong-products-and-why-a-better-prompt-wont-fix-it?source=rss>)

Author: Oleg Kokorin

Published: 2026-11-03T11:25:53Z

Content type: article

Language: en

Sources: [HackerNoon](<https://devfeed.tech/sources/hackernoon.md>)

Topics: [Entity resolution](<https://devfeed.tech/topics/entity-resolution.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [AI-driven information retrieval](<https://devfeed.tech/topics/ai-driven-information-retrieval.md>), [business logic](<https://devfeed.tech/topics/business-logic.md>), [Enhancing AI model performance](<https://devfeed.tech/topics/enhancing-ai-model-performance.md>), [llm-reasoning](<https://devfeed.tech/topics/llm-reasoning.md>)

Tags: [abbreviation](<https://devfeed.tech/tags/abbreviation.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [algorithm](<https://devfeed.tech/tags/algorithm.md>), [algorithms](<https://devfeed.tech/tags/algorithms.md>), [alternatives](<https://devfeed.tech/tags/alternatives.md>), [approach](<https://devfeed.tech/tags/approach.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [automate](<https://devfeed.tech/tags/automate.md>), [automated](<https://devfeed.tech/tags/automated.md>), [business](<https://devfeed.tech/tags/business.md>), [candidate](<https://devfeed.tech/tags/candidate.md>), [catalog-matching](<https://devfeed.tech/tags/catalog-matching.md>), [facets-of-likelihood](<https://devfeed.tech/tags/facets-of-likelihood.md>), [fine-tuning-llms](<https://devfeed.tech/tags/fine-tuning-llms.md>), [llm](<https://devfeed.tech/tags/llm.md>), [llm-prompting](<https://devfeed.tech/tags/llm-prompting.md>), [llms](<https://devfeed.tech/tags/llms.md>), [matching](<https://devfeed.tech/tags/matching.md>), [product-catalog-ai-prompts](<https://devfeed.tech/tags/product-catalog-ai-prompts.md>), [product-matching](<https://devfeed.tech/tags/product-matching.md>), [products](<https://devfeed.tech/tags/products.md>), [prompt](<https://devfeed.tech/tags/prompt.md>), [rag-architecture](<https://devfeed.tech/tags/rag-architecture.md>), [rules](<https://devfeed.tech/tags/rules.md>), [search](<https://devfeed.tech/tags/search.md>), [similarity](<https://devfeed.tech/tags/similarity.md>), [text](<https://devfeed.tech/tags/text.md>)

### AI overview

Product matching errors often come from retrieval, missing data, attribute handling, or business rules rather than prompt quality. The article recommends a staged system: retrieve candidates with lexical and semantic search, extract and normalize relevant attributes, then rank pairs using structured features and category specific rules. Evaluate candidate retrieval separately, since a correct product omitted from the candidate list cannot be selected later. Use configuration for changing rules and synonyms, and retrieve validated historical examples for difficult cases; irrelevant or outdated examples can mislead. Before changing models, check candidate coverage, input completeness, attribute normalization, and rule application. Applying LLMs only to retrieved candidates can lower processing costs but risks missing valid matches; broader processing may improve recall at higher cost. Cloud models have usage fees, while local inference requires infrastructure and maintenance, so compare total costs at the required quality.

### Source excerpt

Why do LLMs match the wrong products? Learn why text similarity falls short and how candidate search, attribute checks, and business rules improve matching.