How I use LLMs
Hi everyone! We've all marveled at the incredible capabilities of Large Language Models (LLMs) like ChatGPT, perhaps even experimented with them for a quick answer or a creative prompt. In a previous deep dive, we explored the fascinating "under-the-hood" mechanics of how these AI trailblazers are trained and how to conceptualize their unique "cognition." Now, it's time to move beyond the theoretical and into the truly transformative.
You might be asking, "How can these powerful tools genuinely enhance my daily life and work?" This article is your answer. Forget generic text generation; we're about to unlock the full spectrum of practical applications, diverse platforms, and advanced functionalities that make LLMs indispensable. Get ready to discover how I leverage these intelligent assistants across various scenarios, and more importantly, how you can integrate them seamlessly into your own routines to boost productivity, spark creativity, and even automate tedious tasks.
We'll journey through the ever-expanding LLM ecosystem, solidify foundational concepts, and then plunge into real-world examples, from tackling everyday queries to mastering complex professional assignments. Next, we’ll explore cutting-edge features and the burgeoning world of multimodal interactions, demonstrating how LLMs are evolving beyond text. By the end, you'll possess a comprehensive toolkit and a fresh perspective on harnessing the true power of AI. So, let's dive into practical mastery and elevate your LLM experience!
Navigating the Diverse LLM Ecosystem: Beyond ChatGPT
Alright, so you've probably heard of ChatGPT, right? It's kind of the Original Gangster of large language models (LLMs), the one that really brought AI into our everyday lives back in 2022. It went viral, and for good reason! It showed us just how powerful a text-based interface could be for interacting with an AI. But get this: the world of LLMs has grown a ton since then. While ChatGPT is still super popular and feature-rich – the "incumbent" as the speaker puts it – there's a whole universe of other amazing LLMs out there now.
Think of ChatGPT as starting the party. Its core function is pretty straightforward: you give it text, and it gives you text back. "The most basic form of interaction with the language model is that we give it text and then we get some type text back in response." Super simple, right? But understanding what's happening behind the scenes, how these LLMs actually work, is fascinating. It's like peeking under the hood of a really cool car.
The architecture of these LLMs involves two main stages: pre-training and post-training. During pre-training, the model essentially "reads the internet." As the speaker cleverly explains, "The pre-training stage is kind of like taking all of Internet chopping it up into tokens and then compressing it into a single kind of like zip file." But here's the kicker: it's not a perfect copy. It's a "lossy and probabilistic zip file" because there's just so much information out there. This is where the model absorbs a massive amount of knowledge about the world, learning to predict the next token in a sequence based on vast amounts of internet data.
Now, this pre-training phase is incredibly expensive and time-consuming, often taking "many tens of millions of dollars say like three months of training." Because of this, it's not done very often. This leads us to a crucial concept: the knowledge cutoff. Since models like GPT-4o were pre-trained months, or even a year ago, their knowledge is a bit out of date. "That's why these models are a little bit out of date they have what's called a knowledge cutof because that knowledge cut off corresponds to when the model was pre-trained and its knowledge only goes up to that point." So, if you're asking about something that happened last week, the model won't have it intrinsically stored. That's where "tool use" comes in, which we'll explore later!
After pre-training, there's the post-training stage. This is where the model gets its personality, its "winning personality" as the speaker humorously notes. "Post-training Stage is really attaching a smiley face to this ZIP file because we don't want to generate internet documents we want this thing to take on the Persona of an assistant that responds to user queries." This stage makes the model act like an assistant, responding to questions and taking on a helpful style. It’s what makes chatting with an LLM feel natural, like a conversation.
Beyond ChatGPT, you've got a whole parade of alternative LLMs flexing their muscles. Big tech companies have jumped in, offering their own versions. We're talking about Gemini from Google, Meta's offerings, and Microsoft's CoPilot. But it's not just the giants! Startups are innovating too. Anthropic has Claude, which is often seen as a direct competitor to ChatGPT. Elon Musk's xAI has Grok. And don't forget international players like DeepSeek, a Chinese company, and Mistral, a French company. You know what's interesting? They all have their own unique offerings and strengths. You can even check out leaderboards like Chatbot Arena or the Open LLM Leaderboard by Hugging Face to see how they stack up.
Another thing to keep in mind is the importance of pricing tiers and model selection. Just like you wouldn't use a bulldozer to plant a flower, you wouldn't always use the most powerful, and therefore most expensive, model available. OpenAI, for example, offers different versions like GPT-4o vs. GPT-4o Mini. Sometimes, a smaller, quicker model is perfectly sufficient and much more cost-effective. Choosing the right tool for the job is key for optimal performance and saving some cash.
Finally, let's talk about the context window. This is basically the LLM's "working memory" during a conversation. "The context window is kind of like this working memory of tokens and anything that is inside this context window is kind of like in the working memory of this conversation and is very directly accessible by the model." Every message you send, and every response the model gives, contributes to this token sequence. When you hit "new chat," it "wipes the token window," effectively resetting the conversation and its memory. This is super important because starting a new chat helps keep your interactions efficient and focused, especially if you're shifting topics or want to ensure the model isn't trying to remember irrelevant details from a previous exchange.
So, when you're interacting with these models, remember this little mantra: "Treat this as your first draft. Treat this as papers to look at, but don't take this as definitely true." They're incredible tools, but they're not infallible. Their knowledge is a probabilistic, "vague recollection of the internet," as the speaker points out. Using them effectively means understanding their strengths, limitations, and how to best guide them.
Unlocking Enhanced LLM Capabilities: Reasoning, Research, and Data Analysis
So, you’ve played around with asking an LLM to write a haiku or summarize a basic topic, right? That’s just the tip of the iceberg! Modern Large Language Models (LLMs) like ChatGPT, Claude, and Perplexity AI have evolved way beyond simple text generation. They're now equipped with sophisticated features that turn them into powerful tools for complex reasoning, in-depth research, and even advanced data analysis. Let’s dive into how these enhanced capabilities can seriously boost your productivity and understanding.
Here's the thing: by default, an LLM is like that "one TB zip file" the expert describes, with knowledge "read in its entirety about six months ago." It's incredibly powerful, but it's just that zip file – no internet access, no calculator, no fancy extras. However, developers have integrated a ton of amazing tools that let these LLMs do so much more than just recall vaguely remembered internet content.
Thinking Models: Beyond Basic Recall
You know what’s interesting? Just like we humans sometimes need to break down a problem, LLMs are getting better at "thinking" through tough challenges. This is where advanced thinking models come in. They leverage techniques like reinforcement learning to improve accuracy, especially when tackling tricky math problems or complex coding tasks. Think of it like a "Pro Mode" for your LLM. For instance, models like GPT-4o and Claude's Extended mode are designed to go beyond a quick, superficial answer. They engage in more iterative processing, improving their reasoning and problem-solving abilities, making them far more reliable for calculations or intricate logical structures where a simple search wouldn't cut it. It’s about more than just remembering facts; it's about solving problems.
Tool Use: Connecting to the Real-Time World
As our expert points out, the LLM’s embedded knowledge has a "knowledge cutoff," meaning it "is a little bit out of date." If you want to know about something that happened "last week," you'll need external help. This is where tool use becomes absolutely crucial. LLMs can now integrate with external capabilities, most notably internet search, to grab up-to-date information.
- ChatGPT's "Search the Web" feature (for paid users) is a prime example. Instead of just relying on its internal, static "zip file" knowledge, it can actively browse the internet to find current data or verify facts. So, if you ask "how much caffeine is in one shot of Americano?" while the model might have a "vague recollection" from its pre-training, it can now search live for the most accurate and current numbers.
- Perplexity AI takes this a step further, often presenting search results and sources alongside its answers, giving you a clearer picture of where its information comes from. This isn't just about getting an answer; it’s about getting a current and * verifiable * answer, something the default "zip file" can't always guarantee. As the expert says, "Treat this as your first draft. Treat this as papers to look at, but don't take this as definitely true." Tool use helps bridge that gap towards verifiability.
Deep Research: Comprehensive Multi-Source Reports
Imagine combining that enhanced "thinking" ability with real-time internet access. That’s the magic of deep research. This capability allows LLMs to not only find information but also to synthesize it from multiple online sources, creating comprehensive reports that would normally take hours of manual research.
- Platforms like ChatGPT's Pro offering and Perplexity's 'Deep Research' mode excel here. They go beyond a single search query, delving into various articles, papers, and websites to gather a fuller picture. You can ask for a detailed report on a complex topic, and the LLM will weave together insights from different perspectives, citing its sources, to present a well-rounded analysis. It's like having a research assistant who can scour the web and write a well-structured paper on demand.
Document Interaction: Summarize, Analyze, Understand
What if the information you need isn't on the open web, but locked away in your own files? Modern LLMs have you covered! You can now upload and interact directly with your own documents, PDFs, articles, or even entire books within the LLM's context window. This capability is a game-changer for information overload.
- You can upload a lengthy research paper and ask the LLM to summarize its key findings, identify the main arguments, or even extract specific data points.
- Need to compare two different legal documents? Upload both and ask the LLM to highlight common clauses or significant differences. It essentially "reads" your documents, integrating them into its current "working memory," making their content "very directly accessible by the model." This functionality transforms your LLM into a personal knowledge manager, helping you digest vast amounts of proprietary or specialized information quickly.
Python Interpreter and Advanced Data Analysis: Code at Your Fingertips
Perhaps one of the most powerful extensions is the integration of a Python interpreter. Initially, the LLM was "a self-contained entity" with "no computer and Python interpreter." But now, some LLMs can actually write and execute code! This opens up a world of possibilities for advanced data analysis and visualization.
- Got a CSV file with sales data? You can upload it and ask the LLM to "calculate the average sales per region," "create a bar chart showing quarterly growth," or "identify outliers in customer spending." The LLM will write the Python code, execute it in a secure environment, and then present you with the results, often including visualizations.
- This makes complex calculations and data manipulations accessible even if you're not a coding expert. Think of the LLM as your data science co-pilot.
However, a crucial caveat remains: always scrutinize the results. Just because the LLM executes code doesn't mean its interpretation or the generated code is flawless. Remember, it's still based on its "vague recollection" and probabilistic nature. Always "go to primary sources and maybe... look up" and "verify that it looks to be roughly right." Despite these incredible advancements, the core principle remains: LLMs provide powerful tools, but human oversight and critical thinking are still indispensable.
These enhanced capabilities truly redefine what LLMs can do for us. From tackling tough problems with improved reasoning to conducting multi-source research and even analyzing data, they’re evolving into incredibly versatile digital assistants, ready to extend their "zip file" brains with real-world tools.
Multimodal LLM Interactions and Quality-of-Life Tools
Alright, so you're probably wondering how you can get more out of your large language models (LLMs) than just typing questions, right? Well, today, we're diving deep into multimodal LLM interactions and some super handy quality-of-life tools that are changing the game. It's not just about text anymore; these models are getting smarter, more versatile, and way more integrated into how we live and work.
One of the coolest advancements is in audio interactions. Gone are the days when you'd have to type every single query. Now, you can use text-to-speech to hear responses spoken back to you, which is fantastic for multitasking or just giving your eyes a break. Even better, speech-to-text lets you simply talk to the LLM, making interaction much faster and more natural. But here's the real kicker: models with 'True Audio' capabilities, like ChatGPT's Advanced Voice Mode or Grok's voice modes, are natively processing your voice. This isn't just converting your speech to text; they're designed to understand nuances in your tone and delivery, making conversations surprisingly fluid and human-like. Imagine explaining a complex idea just by speaking, and getting an intelligent, spoken response back – it’s pretty wild!
Next up, let's talk visuals – image and video input. This is where LLMs truly become next-level assistants. You can now upload images for analysis, opening up a whole world of possibilities. Think about it: you could snap a picture of a nutrition label and ask your LLM to break down its contents, or upload blood test results and get an easy-to-understand explanation. Even something as simple as uploading a meme to get an AI's take on its humor (or lack thereof) is possible! And it doesn't stop there. With growing video capabilities, like those found in ChatGPT on mobile, you can even record snippets of your real-world environment and have the model interpret and interact with it. This could mean pointing your camera at a broken appliance and asking for troubleshooting tips, or getting an instant explanation of a plant you're looking at. It truly blurs the line between the digital and physical worlds.
But what if you need to create visuals? That's where image and video generation comes in. Tools like DALL-E 3, often integrated with leading LLMs, allow you to generate stunning images from simple text prompts. Need a specific graphic for a presentation or just want to visualize a crazy idea? Just ask, and DALL-E 3 delivers. And the world of AI video generation is rapidly evolving, though still in its early stages. Imagine describing a short scene and having an AI create it for you – the potential for content creation, marketing, and even personal projects is immense. It's like having a digital artist and filmmaker at your fingertips.
For the tech-savvy among us, LLM-assisted code development is a game-changer. Integrated Development Environments (IDEs) like Cursor are now leveraging LLMs to make coding more efficient than ever. You can get help with writing code, debugging, and even planning out project architecture. It's like having an expert co-pilot guiding you through every line of code, accelerating your workflow and reducing frustrating errors. This means less time wrestling with syntax and more time focusing on innovative solutions.
Finally, let's explore some fantastic quality-of-life features that truly personalize your LLM experience. We're talking about custom instructions, where you can tell the LLM your preferences, your role, or any ongoing context, so it responds more accurately and in a style that suits you. Then there are memory features, which allow the LLM to retain information from previous conversations, making ongoing interactions much smoother and more coherent. No more repeating yourself! And of course, Custom GPTs – specialized versions of LLMs tailored for specific tasks, much like apps on your phone. These are especially powerful for things like language learning, offering personalized practice and feedback, or streamlining recurring tasks, like drafting routine emails or summarizing daily reports. As the speaker in the video puts it, when talking about verifying information, "Treat this as your first draft. Treat this as papers to look at, but don't take this as definitely true." This advice applies to many LLM uses – it's a powerful assistant, not an infallible oracle.
These multimodal interactions and quality-of-life advancements are truly transforming how we engage with AI. They move LLMs beyond simple text interfaces into a world of rich, intuitive, and highly personalized experiences, making them incredibly powerful tools for everyday life and professional work.
Conclusion
Navigating the dynamic LLM landscape effectively means embracing strategic chat management, discerning model selection, and leveraging specialized tools. From clearing context for focus and cost-efficiency, to employing dedicated 'thinking models' for complex reasoning, or integrating internet search and file uploads for comprehensive data analysis – these practices transform casual interactions into powerful productivity enhancers. Each tip equips you to not just use LLMs, but to master them, turning everyday tasks into opportunities for innovation.
The LLM ecosystem is a rapidly evolving frontier, offering a spectrum of capabilities from basic conversation to advanced analytics. Don't just observe; engage! Experiment with different platforms, explore their unique features, and integrate them into your workflow. Discover the profound efficiencies and creative possibilities awaiting you.
What's the most unexpected or transformative way you've used an LLM, and what features do you hope to see next? Share your insights and let's collectively redefine what's possible.