Content audits are a long-time staple of the user-centred website toolkit, but they take time and effort to complete, which many website owners struggle to find. In my ongoing AI experimentation, I tried using AI tools to help me with auditing web content.
Every day brings a raft of new AI developments. When choosing how to use AI, I’m less sold on getting it to do creative tasks for me because I like doing those myself. Instead, in pursuit of freeing up my time, I prefer to use AI to help me with the tedious tasks I never get around to, the ones I know will take me longer than I think, the ones I put off again and again. Here’s looking at you, content auditing.
Websites are easy to grow but difficult to keep in check
When people come to the UX Service for help with their websites, they tend to use several phrases to describe their site. These have included: ‘It’s a mess!’ ‘It’s a bit out of control’. ‘It’s grown arms and legs’. None of this is unusual when you consider the lifecycle of websites. Over time, websites change hands. Pages once carefully crafted are easily forgotten as new editors take over. New content is created without knowing what’s already there. Very often, content needs to be published to a deadline and it’s quicker to publish a new page than seek out and amend an existing one. Seldomly do publishers want to get rid content that’s taken time and effort to produce.
Read some thoughts on content housekeeping by UX Service team members (current and past):
Digital housekeeping – applying content management practices to improve digital sustainability by me
Be a gardener by Ari Cass-Maran
Content Audit Findings and the 100k Challenge by Milo McLaughlin
How to get a grip of your website (and then keep hold) by Neil Allison
A content audit is the best place to start improving a website (not the homepage)
When people seek to bring order to disarray on a website it’s difficult know where to start, so typically, they start on the homepage with ideas to change images or add new content. While this gives sites a superficial makeover, it doesn’t really help site audiences in search of content. Fewer and fewer website audiences are starting out on homepages, instead they’re parachuting directly into site content from Google search and increasingly, from AI summaries. What does this mean? It’s more important than ever to make sure your web content is up-to-date, accurate, useful and relevant for your website users. Here’s looking at you, content auditing.
Read more about getting your content ready for AI from a recent Content Improvement Club session:
Auditing a website needs a methodical approach (which is where AI can help)
If audits are so important, why are they so easy to put off? Short answer, they can take prolonged amounts of time and concentrated effort. The larger and more complex your site is, it’s likely that there will be more content to review, and the more difficult it may be to maintain a consistent auditing approach. Going through multiple, long pages of web content, it can be easy to run out of the time you’ve allocated for your audit. Breaking the audit into chunks may lead to different results from different sessions as biases creep in. Bringing colleagues in to help can share the load, but can also introduce new perspectives and contradictory auditing decisions. There’s a need to maintain ruthless objectivity, to counter human subjectivity and avoid errors infiltrating. Here’s looking at you, AI.
If you can plan and describe a website content audit, you can ask AI to help you do it
Website content audits can be done for many purposes, but commonly, doing a content audit involves making decisions about what content to keep, what needs to go and what needs to be changed – all in pursuit of reaching a future state of your website. A good way to decide what content to keep is to consider who it’s for and the purposes it serves. If a piece of content doesn’t meet audience needs and doesn’t align with the site purpose, then it’s probably a candidate for deletion. A good content audit of a website therefore typically requires several things:
- An inventory of every piece of content in the site. Typically arranged in a spreadsheet, with one row per webpage. Can be organised into sections or content types, or grouped by root URL
- A note of the site audiences, ideally in order of priority (answering the question ‘Who is this site primarily for?)
- A list of the main reasons those audiences visit the site (answering the question ‘What tasks do people complete on this site?’) together with a list of the main goals of your site (answering the question ‘What do we want people to do on this site?’)
Considering each of these things as different pieces of data, where item 1 is data to be audited, items 2 and 3 are factors or auditing criteria used to decide what happens to those data (typically one of three outcomes: ‘Keep’, ‘Delete’ or ‘Modify’), I reasoned that I could give AI these data, tell it what a content audit was and and ask it to complete this process for a given website.
I experimented with ELM to help me do a content audit of a website
The University’s AI platform, ELM was the natural place to start experimenting with applying AI to aid content auditing. The ELM interface allows you to input a prompt, as well as add additional files and data sources. It also allows you to select different models to handle queries. I was curious to see the differences between the different models – both non-reasoning ones and reasoning ones so I needed to design a prompt that would work for both.
Read more about ELM and its models
ELM website
ELM new model guides (University log in required)
Being mindful of the environmental cost, I wanted to start with a small AI model (Llama 3.3)
AI comes with a significant environmental cost, which is highly dependent on the LLMs you use. Powerful models with more reasoning power are especially resource-intensive to run, so to avoid unnecessary wastage of tokens and other resources I’ve found it’s sometimes best to start with a smaller model and size up accordingly, as the need arises. The trade-off is that smaller models have limited reasoning power, however, so the responses they provide and the tasks they are able to complete can be limited.
Based on my previous experience of using AI and thinking about its potential application to a typical content auditing process, I had a hunch that providing a small model with something like a site inventory (such as a XML sitemap) and detailed data about website audiences and purposes could overload its context window and produce inferior results.
I therefore decided to begin with a lightweight prompt ‘Can you help me do a content audit of this website? (with a link to the website)’ to give a small model something manageable and learn how it approached handling the query. I chose a website that the UX team had recently worked on, so I was familiar with its content, and I entered the prompt, starting with the Llama 3.3 model – an open weights model (costed on computing power rather than per-token usage) which is hosted in University data centres (instead of being cloud-based). I toggled on the option to include web search so ELM was able to access the website.
The same prompt to different ELM models produced varied results
Having starting with Llama 3.3, I moved on to other models: Open AI’s GPT 5 and then Anthropic’s Sonnet 4.6. To assess and critique responses from each, I adopted the mindset of a person reluctant to begin a content audit and looking for AI help to kick-start action on this, and used this as a roleplay to review and compare the responses.
The response from Llama 3.3. was too generic to convince me to start auditing
Despite a glitch in formatting the response, Llama 3.3. provided some general information about content auditing. Its response included an initial assessment of the site, which made objective judgements of the homepage (assessing it as ‘well-structured, with a clear introduction’), the navigation menu (labelling it ‘easy to use with links to key sections’), content quality (noting it as ‘high-quality, informative and engaging’) and accessibility (pulling out that the site had an accessibility statement).
The response then acknowledged that in order to do a thorough content audit on the site in question there was a need to provide guidance on the aspects to focus on, and rounding off, the response noted some of the aspects a typical content audit would examine: content quantity, content quality, accessibility and consistency across different parts of a site.
Reviewing this response, I felt that if I was a site owner looking to improve my site, this response could very easily persuade me that I didn’t need to bother auditing it at all – and I wasn’t left any wiser about how to meaningfully provide guidance on the aspects to focus on if I did decide to proceed with an audit.
Likelihood to convince me to audit: 3/10
Open AI’s GPT 5’s response was detailed and thorough but a bit overwhelming
As would be expected from a reasoning model, the response when I used GPT 5 was more detailed than Llama 3.3’s. Arranged in two sections, the first part included preliminary observations using the site URL provided – which were split into the following categories:
- Information architecture and navigation – commending the site’s positioning on user intents, yet suggesting improvements with more explicit audience pathways and call-to-action signposting
- Content coverage and freshness – using the site’s range of content types to assess content structure and assessing freshness based on recency of news items
- Call to actions – acknowledging that the site had were multiple calls-to-action which could be combined into primary CTAs for each audience
- Accessibility and usability – calling out the need for more meaningful and less duplicated alt text on the site images
- Trust and governance signals – recognising the role of the site’s strong branding to demonstrate authenticity
- SEO – suggesting the site’s page titles and meta descriptions were reviewed for relevance
The second part of the response outlined a plan for a full audit. The plan began by posing three questions to be answered:
- What are the primary goals? e.g. increase visitors, boost traffic, showcase outputs
- Who are the priority audiences? (with suggestions of the different groups)
- What’s in scope? (suggesting either the entire site or specific sections)
The response then provided a suggested series of 7 steps to follow to complete the audit:
- Inventory and crawl (with detail of the inventory to start with and the tools to complete the crawl)
- Editorial quality review (with detail of criteria to score each page – such as inclusivity, accuracy, audience-fit etc)
- UX and IA review (with suggestions to evaluate navigation labels and connections between content and pathways to achieve top tasks)
- Accessibility (with suggestions of accessibility checking tools to use as well as manual checks to complete)
- SEO and technical (with suggestions to check data points like metatags, internal links, robots files and performance)
- Analytics (with suggestions to use analytic data such as bounce rates, page views, site searches etc)
- Recommendations and roadmap (detailing prioritisation measures to make the identified site changes)
It rounded off suggesting a template structure for the content inventory (and offering to create this template as a Google Sheet), an idea for a scoring rubric, and reiterating different tools to use (naming products like Screaming Frog, Google Analytics and SEMrush). To conclude, it offered to do the audit, requesting answers to the three initial questions and asking for access to a sitemap, external tools and governance documents (such as style guide and brand voice guidelines).
Reviewing the response, which was presented as text content, I found it a bit too much to scan and digest, and putting myself in the place of someone facing content auditing with some reluctance, it felt that this large amount of detail could be off-putting to even start the process. I could appreciate the relevance of suggesting of external tools, but knowing that each of those would require setting up accounts and logins, as well as going through a process to integrate with Open AI, it had the effect of making me mentally push content auditing to the bottom of a to-do list.
Likelihood to convince me to audit: 6/10
Anthropic’s Sonnet 4.6’s response gave structured guidance which was easier to follow
The output from the Anthropic model stood out compared to the others from ELM since it was not just plain text, instead, it had been structured with headings, tables and icons. This made scanning and digesting this response much easier than the others.
The response was structured into 10 sections, as follows:
- Site overview – table summarising the site name, owner, primary purposes and date of confirmed content
- Information architecture – list of the top-level navigation elements with an assessment of what worked well and issues identified in a bulleted list
- Content inventory – table of pages, with noted audiences, details of last update (if known) and inferred status (on a red, green and amber scale)
- Calls to action – list of CTAs on the homepage, with assessments and recommendations
- Content freshness – table containing list of most recent items in each section and related assessment
- Accessibility – list of accessible features that were present, table of items that needed checking and recommended accessibility tools to make the assessment
- SEO and technical – table containing an SEO assessment, considering elements like meta descriptions, structured data, mobile responsiveness – each with an allocated status, associated notes and combined recommendations
- Content quality and tone – list of observations and recommendations
- Priority recommendations summary – table of priority actions with an effort/impact score
- To complete the full audit I need – list of requirements to complete the full audit (including sitemap, CMS page list, Google Analytics data, etc.)
Content within each of these sections was comparable to that included in the GPT 5 response, but in the Sonnet 4.6 response, these sections referred directly to information from the site in question – for example, instead of making general assessments of site aspects like navigation and content coverage, it contained direct detail about the site itself with mention of specific pieces of content, noting what was already known (for example, referring to issues noted in the accessibility statement). Arranging this in tables with use of icons and red, green and amber priority indicators made it easy for me to pinpoint what to address and with what urgency, and having this consolidated in the final table was easier to parse than reading it in text format (as in the GPT 5’s response).
Reviewing this response from Sonnet 4.6, it was the best of the bunch from ELM, it contained enough detail to help me get started but not so much that it was overwhelming. Putting myself in the position of a time-poor site owner unsure where to start, I had clear pointers of the priority areas and a mix of quick wins and bigger tasks to choose from.
Likelihood to convince me to audit: 7/10
Three sets of two side-by-side screenshots, showing the output from Open AI GPT 5.5 (on the left) compared to the output from Anthropic’s Sonnet 4.6 (on the right) showing Anthropic’s more structured and visually appealing approach to presenting the responses
As well as ELM, I experimented with Chat GPT and Claude to provide content audit help
Models within ELM can also be accessed through interfaces provided by AI products. Two of the best-known AI products are Chat GPT (by Open AI) and Claude (by Anthropic). I was curious to see the responses each of these products would provide in response to my query asking for content auditing help so I prompted each of them in the same way. In both cases I used the free versions.
Chat GPT gave a suggested approach and produced initial findings
The response from Chat GPT was comparable to the output from ELM choosing an Open AI model, but the response was structured and annotated with tables and icons, in a style similar to the Anthropic response. It began with a suggested audit approach which set out different areas to consider and related questions. In the next section (headed ‘Initial observations’) it contained an analysis of the site itself, which included site strengths and highlighted the following six areas for improvement on the site in question:
- Homepage is quite academic – pulling out some audience questions that would be relevant to be answered on the homepage instead of the content that was there
- Navigation could be more task-focused – noting that the current navigation was driven by organisational structure rather than tasks users would want to complete
- News archive – picking out the need for recent articles
- Calls to action – acknowledging that many of these are worded to provide information not prompt an active response
- Content consistency – advising a uniform page structure for easier reading
- Audience segmentation – recommending clearer delineation between groups of site users
The response concluded with an example audit table and some content recommendations, organised under ‘Keep’, ‘Improve’ and ‘Review’.
Reviewing this response as a whole, I liked the up-front suggestion of an auditing approach and the example audit table which was then supplemented with detail about the site itself and recommendations relevant to different aspects of the site in question. This helped me visualise what the audit outputs could look like, and I could see ways to get there.
Likelihood to convince me to audit: 7/10
A screen showing the output from Open AI after being prompted for help content auditing. The suggested audit approach laid out in a table with columns for the different areas of auditing and associated questions to ask in the right-hand column
Another screen showing the output from Open AI after being prompted for help content auditing. An example audit table is laid out with rows for each URL, and columns containing associated purpose, audience, quality and recommendations.
Another screen showing the output from Open AI after being prompted for help content auditing. Content recommendations are laid out with colour coding for ‘Keep’, ‘Improve’ and ‘Review’.
Claude presented interactives to actively take me through an audit process
Anthropic’s Claude interface took a different approach to the other AI products, immediately asking about the main goal of the audit with options to pick. I picked ‘full inventory’. It then asked the desired breadth of the audit with another set of options, from which I picked ‘full site crawl’. The next screen asked me about the output format, and I picked ‘spreadsheet’.
From top to bottom, the interactive screens produced by Claude asking about the main goal of the audit, the breadth of the audit and the format for the audit output. Each has multiple choice answers to select
After approximately 2-3 minutes, with a running commentary of what was being done, Claude presented a list of ‘headline findings’ of bugs, orphaned content, out-of-date content and broken content (dead links, incomplete calls-to-actions etc). It also produced a downloadable Google Sheet with two tabs – the first containing a summary of the issues to be fixed, their locations and a reason why they needed to be fixed, the second with a full inventory, with one row for every URL in the site and columns logging the page title, content type, date, status (with colour-coded options: ‘Keep’ (green), ‘Update’ (amber), ‘Rewrite’ (dark amber), ‘Review’ (blue) and ‘Critical’ (red)) and recommended actions for each page.
Screenshot of the audit output spreadsheet produced by Claude, showing one row per page URL and different columns logging content types, date, status and recommended actions.
Reviewing this response, it was by far the most effective at making me feel I was starting the content auditing process and supporting me through it. Being presented with the opportunity to answer questions upfront meant I could control how the audit went, and having a spreadsheet created for me to review saved a lot of effort making this from scratch by scraping the site for a sitemap and page metadata.
Likelihood to convince me to audit: 9/10
Conclusion: If you’re putting off auditing your website, try getting AI to help
If you’re looking to improve your website, doing a content audit is one of the best steps you can take – working out what you have, what’s working and what’s not will put you in a strong place to move forward and make positive changes. Depending on how well you know your site, and how much time and resource you have to spend on it, you may appreciate some help to get started auditing its content.
From my quick experimentation with the different AI tools available I learned that, with the exception of the small non-reasoning model, each AI product offered something to help initiate the content auditing process of a website. At a basic level, asking AI to look at your website will pull out something you may not find yourself such as an out-of-date page, an irrelevant CTA or an accessibility glitch. AI can also take a zoomed-out look at your site that you may be too close to adopt yourself – such as reviewing your navigation pathways, appraising your audience segmentation or analysing the consistency of your content voice and tone.
If you’ve never audited a website before, AI can offer a lot of guidance to get you started, and whether you choose to do it yourself taking recommendations from the AI on tools and/or processes or whether you prefer to hand the heavy-lifting to AI to get your audit spreadsheet started – leaving you to deal with the more nuanced decisions that require specialist knowledge and experience, these tools can save you time and effort and may just be the nudge you need to do this all-important website maintenance task.
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