THE SHORT ANSWER
Lists are the safer choice for anything that must survive retrieval. A table encodes meaning in a two-dimensional grid, and when that grid is flattened into a linear string for processing, the connection between a header and a cell can be lost or mangled. A bulleted list with a bolded label followed by its value keeps that pairing intact in every serialisation. Use tables where they genuinely help a reader, then restate the key facts as sentences.
This looks like a formatting preference and is actually a data-integrity question in disguise. Both structures are perfectly readable to a human on screen. The difference emerges at the moment the page stops being a rendered layout and becomes a linear string of text, which is what every retrieval pipeline in existence does to it before anything else happens.
The short version: a list carries its structure inside the sentence, where nothing can strip it out, while a table carries its structure in a grid that has to be reconstructed by whatever is reading it. Reconstruction sometimes fails, and when it fails on a pricing comparison the result is a confident quotation of the wrong number, which is an expensive kind of failure.
The numbers, at a glance
The core problem with tables: the header-to-cell relationship lives in the grid, not the text, and flattening can break it
Why lists hold up: a bolded label followed by its value keeps the pairing inside one linear sentence
Where chunking makes it worse: a table split across two fragments leaves one half with no header row at all
The reliable rule: any fact that must survive should exist somewhere on the page as a complete sentence
What happens to a table during extraction
Text extraction converts a rendered page into a linear sequence. Well-built pipelines detect table structure and convert it into something like markdown or a labelled representation that preserves which value belongs to which column. Less careful ones concatenate cell contents in reading order, which produces a run of numbers with the headers stranded at the front.
You have no way of knowing which treatment your page will get, and the answer differs between vendors and changes between releases. What is consistent is the failure mode: when it goes wrong, the numbers survive and their meaning does not. A stray figure attributed to the wrong column is worse than an omitted figure, because it can be quoted confidently and incorrectly.
Why the definition list is the most robust structure available
Self-labelling. Each item pairs its own term with its own value, so the relationship is in the words rather than in the layout.
Chunk-resilient. If a list is split across fragments, every surviving item still makes sense, because none of them depended on a header row somewhere above.
Format-independent. The pairing reads correctly in HTML, in plain text, in markdown and when read aloud, which matters for voice surfaces.
Naturally quotable. One item is one complete statement, which is exactly the unit a generated answer wants to attach a citation to.
Easy to keep current. Updating one item does not require reasoning about column alignment, so the numbers rot more slowly.
When a table is still the right call
Genuinely two-dimensional comparisons where a reader needs to scan across and down: five products against six attributes, or a specification matrix. Forcing that into a list produces something nobody can use, and human usability is not a secondary consideration. A page that frustrates readers loses the engagement signals that feed back into everything else.
The workable compromise is redundancy. Keep the table for the reader, add proper header markup so a careful parser can reconstruct it, and then write two or three sentences underneath stating the conclusions the table supports. Those sentences are what gets quoted. The table is what convinces the human who clicked through afterwards.
The publishing-platform angle
Some content platforms and rich-text renderers do not support tables at all, or support them so poorly that the output is unusable on a narrow screen. That constraint is less painful than it first appears, and teams working under it often end up with more extractable pages than teams with full formatting freedom, purely because they were forced to express comparisons as labelled lists and sentences.
If you are choosing how to structure a template, the defensible default is: prose for reasoning, labelled lists for paired facts, numbered lists for sequences where order matters, and tables only where the comparison is genuinely a grid. Applying that consistently removes most of the risk described on this page without any further thought.
Auditing the structured content on a page
Find every table and ask whether the comparison is genuinely two-dimensional or just a list in disguise.
Convert any single-column or two-column table into a labelled list with bolded terms.
Give surviving tables proper header cells rather than styled first rows.
Write two or three sentences under each remaining table stating what it shows.
Copy the page as plain text and check that every important number still sits next to its label.
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Related answers
Frequently asked questions
Do AI systems ignore tables completely?
No, and well-built extraction handles them competently. The issue is variance: you cannot tell which quality of handling your page will receive, and the failure mode attaches numbers to the wrong labels. Redundancy in prose costs three sentences and removes the risk entirely.
Is a list better for classic SEO too?
They serve different purposes there. Tables can earn table-format featured snippets for comparison queries, which lists cannot. If a snippet is the goal, keep the table and add the prose summary alongside it rather than choosing between the two. The formats are complementary in classic search in a way they are not in retrieval.
What about pricing tables specifically?
These deserve the most caution, because a misattributed price is a commercial problem rather than a cosmetic one. State each package as a complete sentence naming the package, what it includes and what it costs, then use a table for visual comparison if you want one.
Does image-based formatting cause the same problem?
It causes a worse one. A price or specification that exists only inside an image is invisible to most text extraction, so the system either omits it or falls back on an older figure found elsewhere. Never let a number live only in an image.
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