TL;DR
Claude 3, Anthropic's latest AI model, is set to revolutionize content creation with its advanced language understanding and generation capabilities.
With three models - Opus, Sonnet, and Haiku - Claude 3 offers a range of capabilities, speed, and cost options to suit different needs.
Claude stands out from other language models with its nuanced, analytical, and coherent outputs, guiding readers through its thought process with remarkable clarity.
Experiments demonstrate Claude 3's ability to transform messy, unstructured content into polished, engaging articles, saving content creators time and effort.
By using Claude 3 to impose order on scattered ideas, content creators can focus on high-level creative work that truly matters.
Read to the end for the step-by-step guide to using Claude to create compelling content in a fraction of the time.
Watch the Other Hand…
Another day, another chapter in the OpenAI drama saga. This time OpenAI is trending because of something about a lawsuit and Elon Musk. Yawn.
Ain’t nobody got time for that. There's much bigger (and more practical) news this week with Anthropic releasing the next generation of its LLM, Claude 3.
This is the story that deserves to be in the spotlight, and anyone interested in using AI to get their jobs done faster should be paying attention. Brian Roemmele heralds it as the first artificial general intelligence (AGI) – a machine capable of understanding and learning any intellectual task that a human can. We'll get to that in a minute, but first, a bit of personal backstory.
Claude: A Model After a Content Creators’ Heart
I've been bullish on Anthropic for a long time. Claude feels like an old friend. I've had thousands of conversations with it over the past year and developed a certain sympathy with it that is lacking in the other models. Almost everyone who uses it develops this sense that there's something different happening under the hood.
The biggest difference between Claude and ChatGPT used to be the larger context window (200k tokens), which allowed me to take long transcripts, book chapters, and even full books – my own source material – as the jumping-off point for transforming and refining content.
It has revolutionized the way I do my work. I keep waiting for others to catch up, but this has yet to happen. It remains relatively unknown outside of a small world of AI geeks.
But with other models, including ChatGPT and Gemini, increasing their context windows, this advantage will shrink, and Anthropic will need to distinguish itself on other dimensions.
After playing with Claude 3 for the past two days, it appears they’ve done it. They are ahead of the competition where it matters most to me: efficient, quality content creation.
With Claude 3, content creators can produce high-quality articles, scripts, and even books in a fraction of the time it would take using traditional methods. This will lead to an explosion of new content across various industries, as well as a shift in the role of human writers towards more strategic, editorial, and creative tasks.
When the news of Claude 3 first came out, Erik Schluntz (@ErikSchluntz) and Zack Swiffen (@zswitten) put the model to the test by taking on Andrej Karpathy's (@karpathy) challenge to transform his 2-hour tokenizer video into a textbook-quality blog post. In a single prompt.
So… How did Claude 3 do?
Opus processed the video transcript and screenshots, transcribed code examples, and synthesized the content into a coherent, well-formatted, textbook-”quality” article. This experiment not only showcased Claude 3's multimodal capabilities in handling code, text, and images but also demonstrated its ability to understand and reason when given complex, multi-step prompts.
In fact, Claude 3 is not just one model; it's three models: Opus, Sonnet, and Haiku. Each model offers a unique balance of capabilities, speed, and cost, allowing users to choose the best fit for their specific needs.
Claude 3 Opus: The most advanced and capable model in the family, Opus boasts near-human levels of comprehension and fluency on complex tasks. It excels at analysis, content creation, code generation, and engaging in nuanced conversations across multiple languages. However, this power comes at a higher computational cost and slower response times compared to the other models.
Claude 3 Sonnet: Sonnet offers a balance of capabilities and speed. It's more affordable and accessible to a broader range of users.
Claude 3 Haiku: As the fastest and most cost-effective model in the family, Haiku is ideal for applications that require quick responses and high throughput.
The Growing Gap Between Claude and Other LLMs
Opus, the most powerful model in the Claude 3 family, has demonstrated exceptional performance on various benchmark tests. For example, in the "Needle in a Haystack" test, where a unique sentence about pizza toppings was hidden within a technical document, Claude 3 not only found the sentence but also recognized that it was out of place, even questioning if it was a joke or a test of its attention to detail.
This level of contextual awareness has some people wondering if Claude is on the path to sentience or artificial general intelligence (AGI).
In another impressive feat, Claude 3 scored 60% on an incredibly difficult test where PhDs usually only get 34%. This showcases its ability to tackle complex tasks and outshine even highly educated humans in some areas.
Plus, Claude 3 has even outperformed GPT-4 on certain tasks, highlighting its rapid progress in natural language processing and understanding. As the model continues to evolve, it is poised to make significant strides in various applications, particularly in the realm of content creation, where its ability to generate high-quality, nuanced, and contextually relevant outputs can revolutionize the way content is produced and consumed.
Our Experiment: Writing an Article from a Noisy Transcript
As content creators, we often find ourselves drowning in a sea of scattered ideas, notes, links, transcripts, and images. The challenge lies in organizing this raw material into a coherent and engaging final product. This is where the power of Claude 3 truly shines.
To illustrate this, we attempted to mimic Schluntz and Swiffen’s experiment, using Claude 3 to impose order on a messy podcast transcript. The transcript was from a casual conversation filled with tangents, side stories, and banter—far from a polished piece of content.
Here's a step-by-step breakdown of our process:
Feed the transcript to Claude 3, Opus: We started by providing the model with the raw, unstructured transcript. You can do this by copy-pasting, or uploading a file.
Use multi-step prompting to extract key ideas and big idea: We asked Opus to identify the main topics, themes, and actionable insights from the conversation. The model quickly parsed through the noise and provided us with a clear, concise summary of the most important points.
Generate a structured outline: Next, we prompted Opus to create an outline based on the key ideas it had extracted. The model organized the information into a logical hierarchy, with main sections, subsections, and bullet points. It even suggested relevant subheadings and transitions to help the content flow smoothly.
Refine the outline and draft the full article: Armed with the generated outline, we asked Opus to refine it further and draft the complete article. We provided additional instructions on our desired writing style, tone, and brand guidelines. Opus took these instructions to heart and generated a well-written, engaging article that adhered to our specifications.
Review and compare: Finally, we reviewed the generated article and compared it to the original transcript. The difference was striking. While it still required some editing and a human touch, Opus transformed a rambling, unstructured conversation into a polished piece of content, that spoke to the needs of our audience of content creators.
This is the same process that I lay out in my book, Command the Page: The AI-Assisted Way to Improve Your Writing, Publish Your Ideas Faster & Future-Proof Your Creative Career.
Incidentally, in crafting this article based on the inputs of the podcast transcript, plus additional source material, we thought might be able to get away with being lazy, and try to generate the full article with just one prompt. The results were not very impressive. But when we walked through the multi-step process, the results were a solid rough draft that we could then edit in 30 minutes, rather than the hours it would have taken to write from scratch. This goes to show that even as the technology advances, there’s still no substitute for prompting skills and familiarity with AI in terms of getting the desired outputs.
Writing is thinking, and much of that thinking involves figuring out how pieces fit together. Claude 3 is undoubtedly capable of this type of thinking, in many ways better than a human. It reduces the cognitive load of organizing your ideas, helping you synthesize them in novel ways and make connections between disparate concepts. This is the power of AI for writers and content creators.
Just as Schluntz and Swiffen demonstrated with their Karpathy tokenizer experiment, transforming a noisy video transcript into a well-structured article, our own experiment highlights the potential of Claude 3 to revolutionize the way we create content. By using the model to impose order on our scattered ideas, we can focus more of our energy on the high-level creative work that matters.
Share this post