Why do the various LLM disappoint me in reading requests?

Reddit r/artificial / 4/7/2026

💬 OpinionIdeas & Deep AnalysisTools & Practical Usage

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Key Points

A Reddit user explains that multiple LLMs have repeatedly failed at recommending fictional novels matching specific, detailed reading preferences.
The user reports two failure modes: recommendations that diverge greatly from the request and “hallucinated” book titles/descriptions that do not exist.
They test a known target concept (the Bonesetter series) using 8–10 requested thematic features, but the LLMs do not surface it in results and instead return popular or unrelated titles.
The post questions whether model training biases toward widely known books or whether the task of preference-based fiction recommendation is inherently poorly suited to LLMs.
The user asks for guidance on whether this is a limitation of LLM capabilities for this use case or a problem with how they are prompting the models.

Serious question here. I have tried various LLM over the past year to help me choose fictional novels to read based on a decent amount of input data. I thought this would be a task that fits well into the LLM model but I am constantly disappointed in the suggestions. They are either vastly different from what I requested or complete hallucinations of book titles and descriptions that don't actually exist.

Is the major problem here the training is done on very popular books such that the LLM presents those as a result? I tested this once by starting with the idea in my head of the exact book I wanted to read (in this case it was the Bonesetter series by Laurence Dahners). I described 8 to 10 features I was interested in finding in a book (prehistoric, coming of age, competence porn, etc.) and none of the LLM would suggest this book when I asked for 10 suggestions. They would give Clan of the Cave bear of course, but then off the wall suggestions like Dungeon Crawler Carl or The Martian.

Is this type of task just not in the wheelhouse of LLM or am I doing things wrong?

submitted by /u/Yottahz
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