Reviewing Graphic Design For Dummies

I've been reading and reviewing (here, here, here, and here) books about graphic design.

For book number fourteen, I picked something different. It's produced for a very different teaching style, and was published just last year.

Graphic design for dummies. 2025. Ben Hannam.

I haven't more than flicked through any of the For Dummies books before. I was curious how simple they could actually make the topic.

I ended up having a lot to say about this book, hence a dedicated review post. Not until we get to Meggs' history of graphic design (spoilers) will we see a book with such a contrast of highs and lows!

Writing style and repetitiveness

Graphic design for dummies is extremely readable, in a way that suggests there's a manual of style for this series. The vocabulary is basic and the sentences themselves are seldom complex.

The author often repeats the same thought twice in a row, sometimes with minimal rewording. On page 154, two paragraphs in a row recommend picking a palette of two to five colours. On page 156, the idea that even subtle textures can reinforce design concepts is written two different ways within a paragraph.

Sometimes author Ben Hannam repeats short phrases within a sentence. On page 71, one paragraph states twice that some creatives may feel "threatened" by generative AI; the same paragraph contains the sentence "There are legitimate concerns about copyright and authorship, the quality and uniqueness of the work, and other legitimate concerns" (emphasis added). Page 177 says: "In Figure 8-9, the concept of position hierarchy has been illustrated to help show the concept of position hierarchy." The pinnacle is page 221:

That one is clearly a mistake, but there are so many instances of repetition that I'm not sure it's all errors or filler. It might be Hannam's personal style, or something required by the For Dummies series. Or it might be an artifact of LLM generation — not a possibility I like to bring up, but this is a recent book and the author is deeply pro-AI, as we shall see.

A lot of the repetitiveness serves to reassure. About half of the book's first eight pages of text is just reiterated reassurances for a novice. This returns several times, e.g., on page 75, and I expect it is a feature of the For Dummies series.

Hannam's writing is accessible and clearly-written, with high explanatory power. However, there are a number of awkward phrasings along the lines of "especially if you're first starting out or at the beginning of your journey" (page 2) which align with both the text's repetitiveness and, yes, its grammatical problems.

Errors

What is the state of modern publishing? Usually I reduce it to a few lines of complaints, but this time I'll list the errors that jumped out at me (and then only when I had my notepad nearby).

• "to communication" for "to communicate" (page 41)
• "you may in interested in sharing" (page 53)
• "creating" for "creative" (page 57)
• A cost is itself described as being "pricy" (page 65)
• Page 72 says 360 KB is 180% of 20 KB
• "likes" for "like" (page 74)
• Stray commas (pages 80, 141, 193)
• Wrong product names (page 86)
• "hone in on" (page 96)
• "This may be the first time the client has worked with a graphic designer before" (page 108)
• "ready to go until needed" (page 116)
• Mistaken insertion of "what" (page 131)
• "place" for "placing" (page 134)
• A sentence that's just a collection of verb phrases (page 138) 
• "holes" for "wholes" (page 144)
• "more [...] rather than" for "more [...] than" (page 147)
• "but it can" for "but can" (page 149)
• "a disconnect between [single thing]" (page 151)
• An unnecessary "for you" (page 151)
• "when done so" for "when done" (page 160)
• "ration" for "ratio" (page 164) 
• "land" for "land on" (page 193)
• "technically be" for "be technically" (page 176) 
• "Repetition movement to move to help move your audience's eyes" (page 172)
• Missing "that of" results in comparing a binding method to a book (page 182)
• it/they confusion (page 184)
• "horizontal likes" for "horizontal lines" (page 186)
• "software [...] has already begun to adapt AI tools into their software" (page 241)
• A list of verbs ends with a noun (page 242)
• "of" for "from" and "make" for "take" (page 244)
• Missing "that" (page 197)
• "not to" for "to not" (page 198)
• Missing capital (page 199)
• "in" for "is" (page 206) 
• "and" for "with" (page 220)
• "Chapter 1" for "Bonus Chapter 1" (page 222) 
• Accidental sentence break (page 236)

This was far from a close reading. I doubt these were even a quarter of the errors in the book. There might be a mistake per page on average.

...But no typos that a spellchecker would have picked up. That's the best I can say about it. Anyway, my heart goes out to the book's credited proofreader Debbye, who must have either been under an impossible time crunch or been given a truly nightmarish manuscript if this many errors made it into the final work!

Maybe this is just the new normal. I've gone on about the textual problems in other teaching design books. I've seen elementary typos on official communications from my bank. And at the meta level, even some of the real-world design examples shown in Graphic design for dummies are full of errors, e.g., you can see typos in the barely-visible text in the Raskal packaging and brand guidelines infographic (page 136).

The book's style

Graphic design for dummies spends less ink on real-world examples than the first dozen design books I read. At the same time, it's more pedagogical, and it has a good number of simple illustrative examples. There's a greater focus on presenting and then applying its lessons.

Also unlike those other books, Graphic design for dummies is clearly not laid out spread by spread. Over and over again it breaks examples across spreads, has explanations begin on the spread before the diagrams they refer to, asks you to compare two figures requiring a page turn, and so on. This is more irritating than it sounds, and really drives home the value of designing spreads instead of just flowing the text.

Other parts of the design are good, and it's all teaching-oriented. I like the simple structure, the chapter summaries, and the consistency of chapter layout.

The book's content

Graphic design for dummies covers all the topics I've been reading about for months, and then some. If this was the first text I'd picked up, I think I would have been completely riveted. Even so, I found it engaging and useful.

Ben Hannam has an interesting view of creativity as an additive, expansive process, to be followed (iteratively) by what he calls logical thinking as a process of evaluation and narrowing down. I think 'logic' is the wrong word but see what he's gesturing at. "The goal of logical thinking is to identify the best or right solution. Often, logical thinking is a selective process where constraints dictate certain outcomes". I reflected a lot on how this opening-up / narrowing-down process might relate to game design.

Graphic design for dummies also lays out a seven-part design process which is cyclical and iterative:

1. Project brief and goals

2. Research and planning

3. Brainstorm concepts

4. Sketching and refining ideas

5. Design development

6. Feedback and revision

7. Finalisation and execution

This is reinforced by some incredibly actionable advice given for each phase. This is exactly what I've wanted more of from the design books. For example, phase 4 is about sketching ideas, combining and refining them, and has a really thorough worked example. The book's downloadable bonus chapters (more on them later) continue in this vein.

This is the high point of the book, and it's really strong. I wrote down a lot of notes. Hannam talks about iterating ideas early and often, and brainstorming widely, and I was struck by the sheer extent he recommends. For example, he has his design students make 150 thumbnail sketches for a project, and says he's observed a sketch ratio of 100 likely dead ends : 45 not particularly inspired/exciting/unique : 5 with great potential. I'd really like to take this and run with it as an exercise for both visual design and other creative activities.

Hannam gives solid advice on actually making decisions during the design process. He discusses ways of actually establishing hierarchy in Chapter 9 on layouts, and ways to actually pick a palette (with useful guidance on accessibility) in Chapter 10 on colour. Along the way he scatters in links to online tools, and the book is published recently enough that they work.

If only that was all. 

Design-by-numbers

Generative AI is mentioned throughout, beginning on page 26. Hannam makes his position clear: it is "impressive", "powerful", "extremely easy", etc, and tasks like "editing images [...] can be automated with powerful AI tools."

He uses Adobe Illustrator text-to-vector once for demonstrative purposes (page 71), but then wouldn't you know it, a couple more slip through unlabelled. For example, specimens on page 174 are likely made with the same text-to-vector, because they're grotesque. Graphic design for dummies features characters wearing skin-tone belts and one-and-a-half hats, with misshapen hands, ankles that extend behind instead of into their (mismatched) shoes, and so on.

The book closes with a brief tacked-on chapter on generative AI. Here, Hannam sets out to annoy me with:

• Relentless enthusiasm tempered with a tiny dash of mealy-mouthed both-sides-ing
• Referring to generative AI just as "AI", an acronym he defines then neglects to use
• Remarks about [generative] AI being able to "understand" things

This chapter makes a lot of claims that are, frankly, false.

• "AI can help deliver the content that users need more quickly and accurately than you could by using a set of static variables [in a traditional web storefront]."
• AI will "increase accuracy" and "can analyse data [...] more accurately than humans".
• AI can be used to reduce bias in things like "broad representation of different groups".
• AI can be used to "simulate" user interactions and A/B testing, i.e., fake data instead of gathering it.

This is nonsense, of course. Generative AI increases accuracy? It reduces bias? It's faster than serving static content? Anyone who understands the technology is shaking their head. Hannam loses the last of his credibility in this area by urging you to use [presumably generative] AI to track file changes, do file management, organise assets, and manage versioning. I would uhhhh. Advise against it.

Finally, there's a ton of pandering "will be able to" and "one might imagine" and "are likely to become" which I'm just sick of by now. There's a whole subsection on "AI-Powered Design Assistants" which the author admits is essentially fanfiction! Why on earth did the book get implausible, depressing, futurist fanfiction when a bunch of actual content got delegated to downloadable bonus chapters?

It's a good thing I'm not in the habit of giving numeric scores in reviews, because this chapter completely depleted my goodwill. It's hard to imagine a sourer note to end on... except wait, we're not done. Let's quickly download those bonus chapters.

Oh wait oh no

You thought the AI chapter was a bad look? Check this out.

This book was published less than a year ago. It promises in multiple places, including boldly on the back cover, that you can get six bonus chapters online at dummies.com. Nope! Actually you can't. You can go there and (a) buy this book, or (b) "engage with this book", i.e., type text into a chatbot. There is no way to (c) get the bonus chapters it promised.

Now, Graphic design for dummies provides two publisher links, so I checked those just in case. The first one is for support, booksupport.wiley.com, and it's a dead link! Again, this book was published last year! The other link also doesn't have the bonus chapters. You can send the publishers a message there... but only if you subscribe to them. WTF.

So I looked up author Ben Hannam's website to ask him about it. He has a message box, which formats your message in all caps like you're shouting. Then the Send Message button just throws an error.

 

At this point you just have to laugh. 

I was all set to compare this book to the Ambrose-Harris Vortex, in terms of the density of mistakes and parts of it being fundamentally broken. But ended up going back to the publisher's website on a whim and after some more digging I did find the bonus chapters. Notably not at the dummies.com domain which is where they're meant to be, and where several other For Dummies books have bonus chapters.

The bonus chapters, finally 

There's six bonus chapters, all very consistent with the rest of the book, full of grammatical errors but with some pretty useful content.

Chapter one: Walking through the process of designing a logo and a business card. I really liked these hands-on, in-depth examples. There's a funny typo in the quote on Hannam's mocked-up business card (Figure 9) but it does get fixed in the final result in Figure 10.

And it is so on the nose that it beggars belief that the personal logo Hannam ends up with at the end of his design process appears to read more like the acronym "AI" in lowercase than it does his initials "BH" which it's meant to be:

Chapter two: Strategies for success. Hannam repeats at length the old canard about "Roman war chariots" having led to railway track gauge and thence constrained the design of the Space Shuttle. This is false. Repeating it undermines his point about design constraints and makes the reader wonder what else he's got wrong.

Aside: Ten minutes of proper research puts the myth to rest. But maybe not if you "fact check" it with generative AI. I looked at Google's "search" "results" out of morbid curiosity, and the chatbot both-sides'ed it.

This chapter also has a section called "Escape the pitfall of repeating yourself", which is hilarious in the context of a book which repeats itself so often.

Chapter three: About avoiding common mistakes. It covers aspect ratios, working with images, file management, compression, colours, rich black, and going to press. Nothing new for me personally, but all good simple solid advice.

Chapter four: Exercises to test a new design student's skills. Also really good! I think the intended audience would find it exceptionally useful. I wish it had been included in the physical book instead of hidden away where most readers will never see it.

Chapters five and six: On receiving and giving critique. Mostly in the vein of career advice for a university student.

Hannam wraps up the bonus chapters with a protectiveness about graphic design students being exploited. It helped get me back in his corner a bit after the AI rubbish.

In summary 

What a rollercoaster. Graphic design for dummies has some of the most high-quality practical and focused advice, given in plain language and without talking down to the reader. It's probably the best graphic design teaching text I've read so far, and also stands up okay as an illustrative text. It doesn't really set out to be an inspirational text. And then of course it's error-riddled and full of AI slopaganda, and trying to find the bonus chapters was deeply frustrating.

Despite such deep flaws, I have to say that on the whole, I can recommend this book. But I'm still hoping to find something better.

One more generative AI rant for the pile

(this one's about summarising text)

LLM chatbots – that is AI, in the same sense that we could just start saying "doctors" to refer specifically to orthopaedic wrist surgeons if we collectively decided to – 

LLM chatbots continue to slosh about the world. I used to try them out intermittently to see if they were any good.

My contact with the technology is only incidental these days. To wit:

• If you google old phrases and terminology in English, a LLM chatbot will still confidently weigh in with completely spurious "definitions" because they're not well-represented in the training data.
• If you google modern bits of even slightly less-discussed technical knowledge like "does a Kickstarter project video appear on the prelaunch page", a LLM will still confidently tell you the opposite of the truth.
• If you need customer support or anything that even looks like customer support, there is an extra quarter-hour minimum of wasted bot effort before you can get it.

Nothing I've seen has suggested the technology has fundamentally changed.

 

He can't be wrong, he writes so confidently.

 

In the previous edition of discussing the emperor having no clothes, I mentioned  

[Wikipedia] editors pointed out that the LLM summaries generally ranged from 'bad' to 'worthless' by Wiki standards: they didn't meet the tone requirements, left out key details or included incidental ones, injected "information" that wasn't in the article, and so on

and 

bureaucratic wonks note that genAI can't summarise text. It shortens it and fills in the gaps with median seems-plausible-to-me pablum. The kind you get when you average out everything anyone has ever written on the internet.

I recently saw an AI booster shuffle their position back to "at least it's good for summarising, it's going to completely replace human effort there". With that motivation, let's drill down a bit.

In (a) summary

Let's not bury the lede. LLM chatbots can't produce good summaries. Sometimes by chance yes, but not reliably. Summarising, like everything, is a skill-based task, and of the various capabilities required to do it well, LLMs lack four of the most important.

1. LLMs won't reliably retain important structure or order in which information is presented. They will just haphazardly obliterate implicit linkages. They will even occasionally discard explicit structures, as when the text itself points out that C follows from A and B, and therefore D.

2. LLMs can't identify the most important information in a text (a necessary first step to preserving it in the summary). In a good summary, certain content "should" be retained, certain content compressed, and the remaining content discarded. Vital information generally isn't identified within the text in a way that's detectable without broader context, language skills, and understanding of the world. Even when it is, e.g., in texts where repetition of a word corresponds directly to importance, or phrases like "this is vital information" are always appended, LLMs still aren't guaranteed to retain important details! And the same applies to cutting out unimportant information.

3. LLMs can't stick to the source text, that is, the content they're meant to be summarising. Because they just generate text (by predicting which bits of text should come next, based on an enormous model of which bits tend to come after which bits, hence 'language model'), there's no internal representation of Things 'In' The Language Model versus Things 'In' The Text To Be Summarised, and no impetus to perform computational operations that keep them separate where appropriate. All of which is to say that as well as not including things that should be in a summary, an LLM will readily include things that shouldn't be. Oops

3(corollary). That includes things that aren't true. Oops(corollary)

4. LLMs will sometimes just negate statements for no clear reason. When processing text, e.g. when directed to "summarise", they'll turn a claim into the opposite claim. I think what's going on here is that a statement and its negation are syntactically and semantically similar, even though their meanings are devastatingly dissimilar. Too bad LLM technology doesn't get meanings involved, instead just taking a probabilistic walk through a model of language features like, oh I don't know, syntax and semantics!

Note what these four crucial capabilities have in common. It's the reason why LLMs can't do them. That's right, they require understanding to do properly.

Or if not understanding, then at least computational models of understanding, like formal reasoning over symbolically-encoded domain knowledge including useful axioms. I mention this because classic AI systems (planners, searchers, problem solvers, etc) can do just that, in their various limited ways. They symbolically represent domain information and then perform operations on those symbols which can then give something potentially useful back once related back to domain information.

And those systems are limited, yes, but LLMs don't do 'understanding' at all. As far as I can tell, on the back of a postgrad compsci degree and a few days spent reading and partly understanding the computational basis, this is a fundamental limitation of the technology. One which can't just be fixed, but which would need a whole new (at most LLM-inspired) technology to overcome. For exactly the same reason why AI "hallucinations" can't be fixed.

 

Presummary (a digression)

This technical basis of how LLMs work also explains something else. These chatbots are particularly bad at "summarising" documents which contain surprising content.

By surprising content, I mean...

➡️ Statements seeming to defy common wisdom. Things that are the opposite of statements well-represented in the training data. When X is generally true of a field, but your text describes how ¬X is true of some narrow subfield or specific context, you'll see an LLM "summarise" X into ¬X more frequently.

➡️ Deliberate omissions of things that are usually in correlating training data documents. If your text looks like a text of type blarg, and blarg texts in the training data typically report on X, but you have not reported on X for your own reasons, an LLM is likely to just make something up about X while "summarising".

➡️ Unusual pairings of form and content. Performance degrades the more you ask an LLM to do something novel.

➡️ Context-sensitive language like metonyms and homographs. When X is a big important noun well-represented in the training data and X refers to something else in the text, you'll see an LLM (appear to) get confused by the statements about X its produces for the "summary".

➡️ Nontextual information content. The LM stands for language model. If you have a report that includes and discusses images and diagrams, a chatbot might be able to stop and parse those, and then incorporate its own description of the image as part of the text to be summarised, and maybe even put images back in the summary. But you'll nonetheless end up with a worse output.

 

In summary (but for real)

So LLM chatbots can't be (consistently, reliably, etc) good at summarising.

Of course people who don't know what a good summary is might not notice this; likewise people who possess the skill but don't carefully check the job they told it to do.

(I would argue that in either case, if the task was worth doing to begin with, you should prefer the task not getting done to having no idea whether your document is a good, adequate, or terrible summary)

Anyway this is why you may have seen people who do know what a good summary is point out that LLMs actually "shorten" text rather than "summarise" it. I'm not certain but I think the first time I saw this was in one of Bjarnason's essays.

The sentiment "this technology sure can't do [thing I am skilled at] for shit, but I guess it might be good at [thing I don't know about]" will continue to carry the day as long as people let it

I'll self-indulgently close by quoting myself again:

A lot of people with a lot of money would like you to think that genAI chatbots are going to fundamentally change the world by being brilliant at everything. From the sidelines, it doesn't feel like that's going to work out.

Trying not to be a Gell-Mann Amnesiac

I sometimes wonder how much Gell-Mann Amnesia people experience. Paraphrasing Crichton, when you're a domain expert, you'll sometimes read an article that gets every aspect of your field completely and absurdly wrong, have a little laugh about it... then keep on reading and trusting articles that are about other fields, even from the same publication or writer.

As if they're some pure spring of wisdom which only coughed out a lump of mud when it came to the thing you happen to know about.

It's just an idea from a novelist, not the kind of cognitive bias that's supported by real-world studies that I know of, but you have to admit that it has a kind of... truthiness to it.

Stack this up with Dunning-Kruger and it's easy to become cynical. You might decide that actually, all the loudest voices are talking complete nonsense, all of the time. That might be too far. But I do think it pays to put deliberate hard effort into distinguishing domain experts from overconfident bullshitting pundits.

Now, anyone with their ear to the ground and a weather eye out for Gell-Mann Amnesia should have arrived at the obvious conclusion about generative AI. To wit, that the current state of the technology is that it is an overconfident bullshitter.

On being a piece of software and being confidently wrong

The case studies are easy to find, and the ones from domain experts sound pretty different from the ones from the tech industry and the reporters too busy and/or demoralised to do more than repackage their press releases as articles.

➡️ I am not a historian. The historians I've read say genAI gets softball history questions mostly right and deep ones mostly wrong. Sometimes subtly, sometimes dramatically. It just makes things up when the evidence is scarce. It makes errors of commission and omission as well as having misplaced focus and drawing weird conclusions from premises.

➡️ I am not an artist. The artists I listen to say genAI art looks bland and awful and organic because it doesn't understand composition or anatomy or separate objects (because it doesn't 'understand' anything). It can't make an image that isn't well-represented in the training data, like a camel and a steampunk automaton jousting from the backs of sumo wrestlers. Same in other kinds of media: filmmakers say genAI can't do film because it can't take direction or keep track of characters or have a consistent shot.

➡️ I am not a Wikipedia editor (except incidentally). Earlier this year there was a wretched moment when the Wikipedia editors were going to have genAI article summaries foisted on them, although I think that's turned around now. The skilled editors pointed out that the LLM summaries generally ranged from 'bad' to 'worthless' by Wiki standards: they didn't meet the tone requirements, left out key details or included incidental ones, injected "information" that wasn't in the article, and so on.

➡️ I am not a manager. The managers say genAI can't even collate timesheets reliably.

➡️ I am not a novelist. The novelists say a genAI book reads like a statistical summary of all creative writing anyone has ever done, including all the embarrassing teenage fanfiction. It sucks at originality. And because it doesn't have an internal model or understanding of its outputs, it can't keep track of things and make a coherent satisfying story. Things are vague, tropey, or contradictory.

➡️ I am not a lawyer. The lawyers are, um, well, by the sound of it a lot of them are being sanctioned for using generative AI to cite completely nonexistent caselaw. (☉__☉”)

➡️ I am not a public policy wonk. The bureaucratic wonks note that genAI can't summarise text. It shortens it and fills in the gaps with median seems-plausible-to-me pablum. The kind you get when you average out everything anyone has ever written on the internet. If you try to have an LLM summarise or draw conclusions from a study, it will usually do a bad job, fabricating statements more along the lines of what an average person would guess if they'd only read the study's title.

➡️ I am not a software engineer. The software engineers seem to have mixed opinions. They say that genAI works as code autocomplete (something that has existed for fifty years, but this new kind has pretty sophisticated lookahead, neat). At least some are saying it can't do principled software engineering, it introduces security flaws, its performance drops off for obscure languages, it overconfidently generates bad code, it plagiarises from code repositories that it doesn't have the rights to...

I could go on.

I'm no longer a domain expert in anything, this many years after my stint in academia. I think I'm halfway to being an expert in a few different areas, though. I deliberately concocted some thoughtful questions at the intersection of those areas, just to see.

For example, I asked about the (obvious) mapping of choose-your-path text adventure books onto mathematical graph structures, which the LLM chatbot identified. I followed up with technical questions about the features of those graphs in context: what would the game be like if they weren't digraphs, would you expect cyclic vs acyclic, would a finite state machine be more appropriate and if so why, etc.

And lo, the generative AI output was absurdly, hopelessly, and confidently wrong when given questions that needed expertise.

A lot of people with a lot of money would like you to think that genAI chatbots are going to fundamentally change the world by being brilliant at everything. From the sidelines, it doesn't feel like that's going to work out.

Sometimes I read posts from experts along the lines of

"I've noticed it's almost worthless at [my field], but it sounds like it's pretty useful for [other thing]."

But less so lately, maybe?

So I'm left wondering: are people experiencing massive Gell-Mann Amnesia about these chatbots? Or does everybody know that the emperor has no clothes?

(But oh no, we've invested so, so, so very much money into the emperor's finery, and all the wealthiest people at the imperial court agree: pleeeease could you keep squinting to see this amazing new clothing?)