Almost Timely News: Prompt Engineering and Latent Space (2024-01-21) :: View in Browser
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What’s On My Mind: Prompt Engineering and Latent Space
This week, let’s talk about prompt engineering and latent space. This is a concept that I’m working on for our upcoming Advanced Prompt Engineering Course, which will be a supplement to our Generative AI For Marketers course.
There are a ton of different prompting strategies out there on the Internet, and a gazillion people hawking their secret prompt recipes for whatever amount of money they’re charging. How good are these prompts? Are they worth spending money on? What about all the other prompts people are sharing on LinkedIn and other social networks?
To answer this question, we have to start with latent space. What the heck is this? Latent space is the encoded knowledge of language in a large language model. It’s the stored patterns of data that captures relationships and, when prompted, reconstructs language from those patterns.
Let’s give a tangible example. Suppose you wanted to build a pizza model, an AI that could generate pizza. You’d take photo after photo of pizza after pizza, noting how all the toppings looked. You’d look at the relationships between toppings and cheese, where the toppings are spread, whether they’re on top or under the cheese, what kind of cheese was used, how much sauce was used. You’d measure this from every pizza you could get your hands on, and when you were done, you’d have a database of measurements about pizza. You’d have things like the average number of slices of pepperoni, or how close the jalapeΓ±os are to the onions, or how much pineapple belongs on a pizza.
Then, when someone came to you and said, hey, I want a pepperoni and pineapple pizza, you would go into your HUGE catalog of statistics and query it for pineapple and pepperoni, get some averaged answers about how much of each belongs on the pizza, etc. and you can bake a pizza with those directions.
That database of statistics is the latent space. It’s an understanding of patterns that you can use to generate new outputs. This, by the way, is why the issue of copyright is so tricky with generative AI; the original author’s works, be they words or images, are NOT in the model. Statistical descriptions of an author’s works are, but just like our pizza database contains no actual pizza, a language model or a diffusion model contains no actual original works.
Okay, so the latent space is basically a statistical database. What does this have to do with prompting a language model? All language models are trained from large text databases, like Common Crawl, ArXiv, StackExchange, Wikipedia, Project Gutenberg, etc. Those big databases contain varying amounts of knowledge on a significant number of topics – and the quality of knowledge varies wildly. Just because it’s in Wikipedia doesn’t make it correct, and just because it’s on Blogspot doesn’t make it wrong.
When we write a prompt for a language model, our prompt is ingested by the model and matched up against its latent space, against its database of statistics. It returns a pile of statistics that then get assembled as words, just like a recipe is ingested by a chef’s mind and performed into food.
If we prompt a language model and we get a dissatisfactory response, it’s very likely the prompt we used was insufficient when it comes to the very largest models. But it’s equally possible – and grows more probable the smaller a model gets – that the latent space of the model may not have enough information about what we’re prompting it about.
What happens in that case? The model hallucinates – which is tech speak for drawing on the next set of likely probabilities, even if they are factually wrong. A model that doesn’t know the exact specifics of a prompt because the knowledge isn’t in its latent space will choose the closest match – that’s how models work. We interpret that as a mistake, but the model is actually functioning correctly.
For example, in the early days of language models, when they were trained with relatively small amounts of data and not fine tuned to follow instructions based on millions of examples, you could ask a model who was President of the United States in 1492. We know from history and reasoning capabilities that there was no President of the United States in 1492 because there was no United States in 1492. But a model doesn’t reason – it just assembles probabilities. The President of the United States is a person, and typically a prominent person (unless you were President Taylor or President Van Buren, names no one can seem to remember). 1492 is associated for good or ill with a prominent person, Christopher Columbus. In the absence of a factually correct statistical match, early language models replied that Christopher Columbus was President of the United States in 1492. Statistically, a sensible answer even though it’s factually wrong.
A key part of advanced prompt engineering is knowing the limitations of a language model’s latent space. You have to assess its latent space for a given topic to know what it knows on that topic – assuming it’s important enough for you to want to use generative AI in the first place – before you can start constructing prompts. Otherwise, you will prompt it for things it doesn’t know well, and the answers you get back will have a high chance of hallucination. They’ll be statistically correct under the hood, but factually wrong from a reasoning standpoint.
Going back to our pizza analogy, suppose you gave your pizza chef a request for a pizza with ham and pineapple, but our chef had never heard of a pineapple. Chef knows that from our description, pineapple is a tropical fruit, a sweet fruit, and a yellow fruit, so chef makes us a pizza with their best guess:
image generated with DALL-E 3 via Microsoft Bing Image Creator
…a ham and banana pizza. You can see how, from a descriptive characteristics perspective, pineapple and banana might be thought of similarly, but… no. If you think pineapple doesn’t belong on pizza, banana REALLY doesn’t belong on pizza.
But that’s a concrete example of prompting a model for something that isn’t in its latent space, isn’t in the database of knowledge that it has, and it substituting the next closest thing that seems rational and logical, but is very much not the same thing.
How do you assess a model’s latent space? By asking it about what it knows on a topic, especially deep into the topic. If you know the topic well, you can ascertain just how deep a model’s knowledge goes before it runs out of knowledge and starts to hallucinate. For example, I started with this very, very technical prompt:
Describe the key characteristics of the SARS-CoV-2 JN.1 clade in terms of the L455S mutation.
When I ran this in Chatbot Arena, one model said the JN.1’s parent lineage is BA.2.86, while another model said JN.1 is also known as BA.2.75:
The second model’s response is factually incorrect – JN.1 comes from the BA.2.86 lineage. The model hallucinated, meaning that its latent space doesn’t know about what the JN.1 clade actually is.
What do you do when you evaluate a model and find its limitations? Latent space is basically the database that the model draws from, so if you find out a model lacks knowledge on a topic, you have to provide that knowledge. That means incorporating the knowledge either in the prompt itself, or through uploading data and documents like in ChatGPT and Custom GPTs. By providing the data you want the model to use, you are effectively increasing the latent space of the model and reducing the likelihood that it’s going to hallucinate on you.
This is the key part that prompt engineering guides overlook: no matter how good your prompt is, if the model doesn’t have knowledge of what you’re prompting, your prompt will not perform well. It’s like asking a chef to cook with ingredients they don’t know. You can be incredibly clear in your instructions, but if the chef has no knowledge of what you’re asking, you will NEVER get a satisfactory result without providing the ingredients for the chef (and maybe making it for them a couple of times so they can actually taste it themselves and understand it).
This is also why prompts should generally be associated with specific models; the prompt I used above would best be used in models that know what the JN.1 clade is, and should not be used in models that are unaware of it. Now, for common, old topics like management skills or personal finance, a prompt is probably fairly portable. But the deeper a dive you need to do, the more specific you’ll need to be about which model to use with prompts on the topic – and which supplementary data you’ll have to provide, no matter what.
Finally, apparently no one likes the idea of banana on pizza. I’m not thrilled with it either.
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ICYMI: In Case You Missed it
Besides the new Generative AI for Marketers course I’m relentlessly flogging, I recommend the piece on legality of works in Custom GPTs, made with the assistance of 3 actual lawyers.
- You Ask, I Answer: Legality of Works in Custom GPTs?
- You Ask, I Answer: Retrieval Augmented Generation vs Fine-Tuning?
- You Ask, I Answer: Should Generative AI Be In Your Product?
- Mind Readings: Climate Change is Structural Inflation
- Almost Timely News, January 14, 2024: The Future of Generative AI is Open
- Mind Readings: AI Ethics Inside Language Models
- Now With More Fake Comments
- So What? Generative AI Analytics
- In-Ear Insights: Unlocking Workforce Potential with Generative AI
Skill Up With Classes
These are just a few of the classes I have available over at the Trust Insights website that you can take.
Premium
- π¦Ύ Generative AI for Marketers
- π Google Analytics 4 for Marketers
- π Google Search Console for Marketers (π¨ just updated with AI SEO stuff! π¨)
Free
- βοΈ The Marketing Singularity: How Generative AI Means the End of Marketing As We Knew It
- Powering Up Your LinkedIn Profile (For Job Hunters) 2023 Edition
- Measurement Strategies for Agencies
- Empower Your Marketing With Private Social Media Communities
- Exploratory Data Analysis: The Missing Ingredient for AI
- How AI is Changing Marketing, 2022 Edition
- How to Prove Social Media ROI
- Proving Social Media ROI
- Paradise by the Analytics Dashboard Light: How to Create Impactful Dashboards and Reports
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If you work at a company or organization that wants to do bulk licensing, let me know!
Get Back to Work
Folks who post jobs in the free Analytics for Marketers Slack community may have those jobs shared here, too. If you’re looking for work, check out these recent open positions, and check out the Slack group for the comprehensive list.
- Data Platform Engineer (Experienced) at Clinician Nexus, Inc.
- Deep Learning Scientist – Generative Ai at Barrington James
- Direct Marketing Lead at Electrolux Group
- Director Of Content Marketing at Outreach
- Ecommerce Analyst at Henry Schein Dental UK
- Ecommerce Media Sr. Specialist at Electrolux Group
- Generative Ai Engineer at Smart IT Frame LLC
- Logicmonitor at LogicMonitor
- Senior Product Manager – Ehr, Generative Ai, Health-Tech Stealth Co at Producto
- Spezialist*In Kommunikation Comdirect at Commerzbank AG
What I’m Reading: Your Stuff
Let’s look at the most interesting content from around the web on topics you care about, some of which you might have even written.
Social Media Marketing
- Social Media Publishing: What Is Social Publishing? What Are Its Basics?
- Social Media Case Studies: Get Inspired to Prove ROI in 2024
- Social Media Posting Schedule Guide via Agorapulse
Media and Content
- A 2024 Guide to the YouTube Algorithm: Everything You Need to Know to Boost Your Content
- Content clusters: What are they and do you need them? Yoast
- By the numbers: This is how many pitches actually get responses via PR Daily
SEO, Google, and Paid Media
- Google Responds To Claims Of Garbage AI Content In Google News
- How Many Keywords Should You Track for SEO in 2024? via Portent
- 14 Ways to Improve Ecommerce Product Pages for SEO
Advertisement: Business Cameos
If you’re familiar with the Cameo system – where people hire well-known folks for short video clips – then you’ll totally get Thinkers One. Created by my friend Mitch Joel, Thinkers One lets you connect with the biggest thinkers for short videos on topics you care about. I’ve got a whole slew of Thinkers One Cameo-style topics for video clips you can use at internal company meetings, events, or even just for yourself. Want me to tell your boss that you need to be paying attention to generative AI right now?
πΊ Pop on by my Thinkers One page today and grab a video now.
Tools, Machine Learning, and AI
- Zuckerberg Says Meta Is Pushing Hard To Lead the AI Future via Social Media Today
- Fairly Trained launches to certify AI tools trained on licensed data | VentureBeat
- OpenAI CEO Sam Altman is still chasing billions to build AI chips via The Verge
Analytics, Stats, and Data Science
- Why Web Analytics Is Important For Digital Marketers via Simplilearn
- TaskWeaver: A code-first agent framework for efficient data analytics and domain adaptation via Microsoft Research
- 55 Instagram Statistics And Facts For 2024
All Things IBM
- How one IBMer is creating a more sustainable future for the island where he grew up via IBM Blog
- Chatbot examples: A beginners guide via IBM Blog
- Accelerating sustainable modernization with Green IT Analyzer on AWS via IBM Blog
Dealer’s Choice : Random Stuff
- The Internet Is Being Ruined by Bloated Junk via The Atlantic
- Are Companies Getting Away with ‘Cheap Talk’ on Climate Goals? via HBS Working Knowledge
- The Surprising Secret to Stand-Out Ideas – Tamsen Webster
How to Stay in Touch
Let’s make sure we’re connected in the places it suits you best. Here’s where you can find different content:
- My blog – daily videos, blog posts, and podcast episodes
- My YouTube channel – daily videos, conference talks, and all things video
- My company, Trust Insights – marketing analytics help
- My podcast, Marketing over Coffee – weekly episodes of what’s worth noting in marketing
- My second podcast, In-Ear Insights – the Trust Insights weekly podcast focused on data and analytics
- On Threads – random personal stuff and chaos
- On LinkedIn – daily videos and news
- On Instagram – personal photos and travels
- My free Slack discussion forum, Analytics for Marketers – open conversations about marketing and analytics
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Events I’ll Be At
Here’s where I’m speaking and attending. Say hi if you’re at an event also:
- Tourism Industry Association of Alberta’s Tourism Summit, Edmonton, February 2024
- Independent Consortium of Booksellers Association, Denver, February 2024
- Social Media Marketing World, San Diego, February 2024
- MarketingProfs AI Series, Virtual, March 2024
- Australian Food and Grocery Council, Melbourne, May 2024
- MAICON, Cleveland, September 2024
Events marked with a physical location may become virtual if conditions and safety warrant it.
If you’re an event organizer, let me help your event shine. Visit my speaking page for more details.
Can’t be at an event? Stop by my private Slack group instead, Analytics for Marketers.
Required Disclosures
Events with links have purchased sponsorships in this newsletter and as a result, I receive direct financial compensation for promoting them.
Advertisements in this newsletter have paid to be promoted, and as a result, I receive direct financial compensation for promoting them.
My company, Trust Insights, maintains business partnerships with companies including, but not limited to, IBM, Cisco Systems, Amazon, Talkwalker, MarketingProfs, MarketMuse, Agorapulse, Hubspot, Informa, Demandbase, The Marketing AI Institute, and others. While links shared from partners are not explicit endorsements, nor do they directly financially benefit Trust Insights, a commercial relationship exists for which Trust Insights may receive indirect financial benefit, and thus I may receive indirect financial benefit from them as well.
Thank You
Thanks for subscribing and reading this far. I appreciate it. As always, thank you for your support, your attention, and your kindness.
See you next week,
Christopher S. Penn
You might also enjoy:
- Almost Timely News, January 28, 2024: Copyright Must NEVER Apply to AI-Made Works
- Almost Timely News: Recipes vs. Principles in Generative AI (2024-03-03)
- You Ask, I Answer: AI Music Collaborations and Copyright?
- You Ask, I Answer: Retrieval Augmented Generation for Tax Law?
- Almost Timely News: Principles-Based Prompt Engineering (2024-02-25)
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Christopher S. Penn is one of the world’s leading experts on AI in marketing. Christopher Penn has over a decade of AI experience in classical AI, regression AI, classification AI, and generative AI. Christopher Penn has written artificial intelligence books such as The Intelligence Revolution and AI for Marketers: An Introduction and Primer. Christopher Penn is an AI keynote speaker around the world.
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