AI~Literacy: Howto stay grounded while choosing your AI Model
Capability building is more then passing on knowledge; it’s about experiential learning—applying concepts in real-world scenarios while balancing AI-saturated efficiency with human sense making.
This blog series delves deeper into the intersection of technology and human development, highlighting how you can build your resilient adaptiveness to thrive in this digital age.
Most organizations are interested to deeper exploring the benefits and pitfalls of AI,… Some are clear about the outcomes they expect from their AI investments. What’s not clear for many is how to go about making these outcomes a reality. Not all AI models are the same, and neither are your use cases.
In this post we’ll clarify what a multi-model approach is and how to apply it in real business terms. So, let’s explore in 33 simple steps how to curate AI models that address your specific business needs.
Growing Smarter
Think of your business like a garden. You don’t plant only carrots. You grow what you need, based on the season, soil, and purpose. Working with AI models is kind of like that. You can’t just rely on one model to do everything. Instead, you want to choose the right mix of models—each suited to a specific job. That’s called a multi-model approach. But how you use it matters more than ever. There’s no one-size-fits-all model. What works for one task might not work for another. That’s why I believe smart businesses can combine two ideas:
Fine-tuning: making an AI model work better for your specific needs.
Multi-model approach: using different models for different jobs.
First, What’s an AI Model?
An AI model is a tool that helps software think and act, like a digital brain that helps software understand and perform tasks—like answering questions, summarizing documents, or finding patterns in data.
Some models are big and powerful. Others are smaller and faster. Some are trained on general topics. Others can be fine-tuned to match your business. Different models are trained to do different things, and they all come with their own strengths, weaknesses, and costs. That’s why choosing the right one matters.
Why Fine-Tuning Helps
General models are trained on data from the whole internet. They’re good at many things. But not great at your thing.
Fine-tuning adjusts a model using your business data. It learns your language, your processes, and your goals. That makes it more accurate and more useful.
You don’t need to start from scratch. You just train an existing model a bit more—with your stuff.
Benefits of Fine-Tuning:
Better results on your tasks
Lower costs than building a model from zero
Faster to set up
What Is a Multi-Model Approach?
A multi-model approach means using more than one AI model in your business. You pick and mix different models depending on the task, just like choosing the right tool for a job.
One model might be great at writing emails.
Another might be better at reading legal documents.
A third might help summarize customer support chats.
Step 1: Start with a Clear Prompt
Before choosing any model, you need to know what you want it to do. That starts with writing a prompt.
A prompt is a clear instruction or question you give to the AI.
For example: “Summarize these 10-page customer complaints from the past week in 5 bullet points.”
Be clear about what the model should do and what a good result looks like.
Good prompts help you describe:
What problem you’re solving
Who it’s for
What the AI should deliver
What a “good result” looks like
Think of this as telling your garden what you want to grow. Carrots? Tomatoes? Lettuce? Your prompt defines the goal.
Step 2: Research and Compare Models
Now it’s time to shop around.
You’ll want to look at different models and ask:
Who made it?
What kind of data was it trained on?
How accurate is it?
What is the knowledge cut-off date, which is the last date their training data includes information?
How fast is it?
How much does it cost to use?
Are there risks (like bias or security issues)?
Can it work with your existing tools & systems?
Can you fine-tune it?
You don’t need to know all the technical details. It’s more like comparing different cars—each with its own size, fuel efficiency, and features. Just think of it like picking the right tool for the job.
Step 3: Test Different Models
Start with a bigger, more advanced model to see if it can handle your prompt well.
Then, try the same prompt on smaller, simpler, cheaper ones. See if you get similar results.
Why? Because bigger isn’t always better. Smaller models are often cheaper and faster—and they might still give you what you need.
Testing helps you see what works best for your actual use case.
Step 4: Keep Evaluating as You Go
Choosing a model isn’t a “set it and forget it” decision.
Like tending a garden, you’ll need to:
Monitor how the model performs over time
Watch for changes in cost or speed
Adjust your prompts if your needs shift
Try out new models as they become available
AI models change & evolve fast. Your needs will too. Keep checking and improving. So your strategy needs to keep up too.
What Should You Look for in a Model?
Here are a few things to keep in mind when picking the right model for your business:
Performance: Does it get the job done accurately and consistently?
Speed: Does it deliver results quickly enough for your needs?
Size: Bigger models can be more powerful but also more expensive.
Transparency: Can you understand how the model makes decisions?
Deployment: Can you use it easily with your current systems?
Risk: Are there security or ethical concerns?
Think of this as checking the health of your garden plants: Are they thriving? Or do they need some TLC? You know: Everyone loves a little tender loving care…
Bring Your Teams Together
Implementing AI isn’t just an IT project. It’s a team sport.
You’ll want people from different departments—IT, operations, marketing, legal—working together to make sure the models support your business goals.
Each person brings a different perspective. And that’s essential when evaluating performance, understanding how results are used, and identifying when something feels “off.”
Track the Right Metrics
To know if your AI is doing its job, track key metrics like:
Task success rate
Speed of response
Cost per use
Accuracy
User satisfaction feedback
These numbers help you make smart decisions about whether to keep using a model, adjust it, or try something new. Keep people in the loop—especially for high-risk tasks.
What If Something Goes Wrong?
AI models aren’t perfect. They can make mistakes or behave in ways you didn’t expect.
That’s why you need clear accountability and error-handling plans.
Ask:
Can someone review or override the AI’s decision?
Can you trace where the decision came from?
What happens if the model goes off course?
Keeping humans involved—especially in high-stakes situations—is critical.
Will This Work for Every Industry?
Some industries—like customer service, e-commerce, and HR—are already seeing big benefits from AI.
Others—like healthcare, law, or education—might take longer because decisions are more complex and sensitive.
But in all cases, human skills still matter: creativity, empathy, and ethical judgment can’t be automated. AI can support work, not fully replace.
Grow Your AI Garden with Care
Using AI effectively is a lot like gardening:
You need to know what you’re planting (your prompt).
You need to pick the right crops for your soil (the right models).
You need to tend and care for your garden (evaluate, adjust, and improve).
And you need a good team to help harvest the results (your cross-functional crew).
A multi-model approach gives you the flexibility to build smart, resilient AI systems—ones that grow with your business and adapt to changing needs.
So don’t settle for one-size-fits-all AI.
Don’t rely on one model for everything.
Don’t overbuild.
Start small, test often, and keep evolving. Adjust with reflection & grow.
Fine-tune where it helps.
Mix models where it makes sense.
Keep people involved.
That’s how businesses build smarter, faster, and more flexible AI systems—ones that work in the real world.
How do I personally benefit of explainable & accessible AI @ home?
I stumbled upon Nomic.ai. They share a free GPT4All Desktop Application allowing you to download and run large language models (LLMs) locally & privately on your device.
With GPT4All, you can chat with models, turn your local files into information sources for models (LocalDocs), or browse models available online to download onto your device.
GPT4All makes working with advanced language models surprisingly easy and accessible. Unlike many AI tools that rely on cloud servers, API keys, or expensive GPUs, GPT4All runs entirely on your own laptop or desktop. That means no internet connection is required, no data leaves your device, and you don't need a high-end machine to get started. Whether you're writing, coding, learning, or experimenting, GPT4All offers a practical, privacy-friendly way to use powerful AI tools—right from your own computer. Looks like a good start to further explore. What do you think?
Your homegrown AI garden—like your business—deserves to thrive.
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