English | Size:
Genre: eLearning
LLMs are powerful.
Especially for the kinds of “fuzzy” tasks that are similar but not identical. The kinds of tasks that make work tiring, annoying, or difficult: Gather all this info up and find the related parts. Break down and prioritize this request. Prep for this meeting, then follow up after. This task got lost; when did we drop this from our conversations across email, Slack, Github comments? Now please file this ticket.
It’s not that LLMs can do all the work for you, but they can be an incredible support that empowers you to focus on the work only you can do. Just like the very best executive assistant.
But generic chatbots (the big commercial ones) don’t really give you access to that power. And they don’t offer particularly high-quality output, either. They simply can’t; they try to be all things to all people. Millions of users can’t all have a custom experience crafted just for them. The context windows can only be so big. You can only load so much data. Prompts can only get you so far. They’re not configurable. You can’t tinker with the guts. It’s a black box.
black box
The thing is, these chatbots are not “the LLM” itself, they’re an interface between you and the model.
And it’s that interface, the system, that matters most. When you use a big name chatbot, it’s the system that makes all the choices about what data to pass to the model and when, how to shape and handle the responses, what to remember, and more.
Choices you don’t know about, can’t find out about, and certainly can’t change.
All the custom prompts in the world can’t overcome the power of their system.
When you want to fully grasp the power of bespoke AI, you’ve gotta build your own system: a tool that uses your data, learns your preferences, remembers what you tell it, interacts with your tools, and feeds all that into state of the art models.
And, despite what you may have heard, you don’t need to train your own custom model!
In fact, the model itself is almost irrelevant!
The system is where the overwhelming majority of smarts are: the data handling, the context management, the tools.
You can build your own system. One that learns from you and remembers. One that uses complex and sophisticated techniques to search, interpret, and analyze your data. One that you tune and control.
Your system can absolutely trounce a commercial chatbot in terms of function and fit for purpose.
When you roll your own system, your very own bespoke assistant, you can unlock the true power of LLMs in three ways:
Retrieval. You give your assistant access to your own data (heaps of it, instead of the little bit you can paste into a textbox) which it provides intelligently to the model underneath, as-needed
Memory. You give your assistant a persistent “memory” (so it can remember statements, preferences, interactions between sessions) which it uses to “remind” the model of just the most salient facts, helping it tailor its outputs to you
Agents + Human-in-the-Loop. You build agents that can access tools and APIs for your favorite apps and services, and use your assistant (your system) to delegate this access, controlled by human sanity-checking, to the model
That’s the makings of an ideal personal assistant. Or, frankly, any kind of custom LLM-powered tool.
And you don’t need a data center, or a bunch of GPUs, or a machine learning degree to build it.
You can use off-the-shelf models, tools, and AI engineering patterns to achieve amazing results.
And in my new cohort-based workshop, that’s exactly what you’ll learn how to do.
trbt.cc/xrvu4krv2a92/AIHero-BuildYourOwnAIPersonalAssistantinTypeScript2025-12.rar.html
If any links die or problem unrar, send request to
forms.gle/e557HbjJ5vatekDV9