Dialogue Is the Best Teacher
For those new to AI, direct dialogue provides the most intuitive way to understand these systems' capabilities. Through thoughtful conversation with varied prompts, beginners quickly develop a feel for how AI models think and respond—revealing insights impossible to gain any other way.
The recent explosion of AI capabilities has left many newcomers bewildered about how to effectively engage with these systems. Unlike traditional software with buttons and menus, large language models like Claude, ChatGPT, and others operate through conversation—making dialogue not just the interface, but the fundamental way to understand their capabilities and limitations.
I've found that many people approach AI systems with either excessive caution or unrealistic expectations. The truth lies somewhere in between, and the best way to discover this middle ground is through active experimentation and dialogue.
The Dialogue Difference
Reading about AI capabilities can only take you so far. Models respond differently based on how you phrase questions, how much context you provide, and what examples you include. This dynamic nature means that hands-on experience becomes invaluable.
Consider how different these approaches might be:
- Asking "What is machine learning?" (getting a textbook definition)
- Saying "Explain machine learning as if I'm a high school student" (receiving a more accessible explanation)
- Engaging with "I'm confused about the difference between supervised and unsupervised learning. Can you explain with examples from everyday life?" (obtaining a tailored, contextual response)
Each approach yields different results, demonstrating how your input shapes the AI's output.
Starting Simple: First Conversation Examples
For those new to AI interactions, here are some starter prompts that reveal interesting aspects of how these systems work:
Example 1: Testing adaptability to feedback
User: Can you write a short paragraph about climate change?
AI: Climate change represents one of the most pressing challenges facing
humanity today. Rising global temperatures, increasingly severe weather
events, and shifting climate patterns are disrupting ecosystems and
threatening communities worldwide. Driven primarily by human activities
that release greenhouse gases into the atmosphere, this complex phenomenon
requires urgent collective action, technological innovation, and policy
changes to mitigate its worst effects and protect vulnerable populations.
User: That was good, but could you make it less academic and more conversational?
AI: You know, climate change is really shaking things up across the planet
right now. We're seeing hotter temperatures, wild weather swings, and
patterns that throw nature out of whack—and it's affecting people everywhere.
Most of this comes from all the greenhouse gases we humans pump into the air.
The thing is, we need to tackle this together—with new tech, smarter policies,
and looking out for folks who are most at risk. It's a big challenge, but one
we can't afford to ignore.
This exchange demonstrates how AI models can adjust their communication style based on feedback, showing their ability to modify outputs to better match your needs.
Diving Deeper: Exploring Capabilities
Once comfortable with basic interactions, try prompts that explore more sophisticated capabilities:
Example 2: Testing reasoning abilities
User: I'm planning a dinner party for 8 people. Three are vegetarians, one has a
gluten allergy, and one doesn't eat dairy. Can you help me plan a menu where
everyone has options?
AI: I'd be happy to help with your dinner party menu! Here's a plan where
everyone can enjoy multiple options:
APPETIZERS:
- Veggie platter with hummus and olive tapenade (works for everyone)
- Rice paper rolls with vegetables and herbs (works for everyone)
MAIN DISHES:
- Roasted vegetable and quinoa stuffed peppers (vegetarian, gluten-free)
- Herb-roasted chicken with lemon (for meat-eaters, gluten-free)
- Mushroom and white bean ragout (vegetarian, gluten-free, dairy-free)
SIDES:
- Mixed green salad with citrus vinaigrette (works for everyone)
- Roasted potatoes with rosemary and garlic (vegetarian, gluten-free, dairy-free)
- Steamed asparagus with olive oil and lemon (works for everyone)
User: I just remembered one guest also doesn't eat nightshades. How would you
modify this menu?
AI: No problem! Here's your revised menu avoiding nightshades (tomatoes, peppers,
potatoes, eggplant):
APPETIZERS:
- Cucumber and carrot sticks with hummus and olive tapenade (works for everyone)
- Rice paper rolls with cucumber, carrots, avocado and herbs (works for everyone)
MAIN DISHES:
- Stuffed portobello mushrooms with quinoa, spinach and herbs (vegetarian,
gluten-free)
- Herb-roasted chicken with lemon and thyme (for meat-eaters, gluten-free)
- Mushroom and white bean ragout with zucchini (vegetarian, gluten-free,
dairy-free)
SIDES:
- Mixed green salad with citrus vinaigrette (works for everyone)
- Roasted sweet potatoes with rosemary (vegetarian, gluten-free, dairy-free)
- Steamed green beans with olive oil and lemon (works for everyone)
This exchange tests the AI's ability to solve problems with constraints and then adapt when new information changes those constraints—a core capability of modern systems.
Uncovering Limitations Through Dialogue
Perhaps the most valuable learning comes from discovering what AI models struggle with. Try these revealing exchanges:
Example 3: Testing epistemological awareness
User: What happened in the 2028 Olympics?
AI: I don't have information about the 2028 Olympics as they haven't occurred
yet. My knowledge has a cutoff date, and I don't have access to future events.
I can discuss the history of past Olympics or what's been officially announced
about future plans, but I can't provide results or specific details about
events that haven't happened.
This exchange reveals how models handle questions about information they don't possess, showing their ability (or inability) to acknowledge the boundaries of their knowledge.
Learning Through Chain Reactions
One of the most enlightening approaches is creating a chain of prompts that build on each other:
Example 4: Building a conversation thread
User: Can you explain what a neural network is?
AI: A neural network is a computing system inspired by the way our brains work.
At its core, it's a collection of connected units called neurons that process
and transmit information. Each connection can transmit a signal from one neuron
to another, and the receiving neuron processes the signal and signals neurons
connected to it.
What makes neural networks powerful is their ability to learn from data. During
training, the connections between neurons are adjusted based on the data they
process. These adjustments allow the network to recognize patterns and make
decisions with minimal human intervention.
Think of it like a complex system of dominoes that can rearrange themselves
based on what patterns they're trying to detect.
User: So how is that different from how our brains work?
AI: Great question! While neural networks are inspired by the brain, there are
several key differences:
Scale and complexity: The human brain has roughly 86 billion neurons with
trillions of connections. Even our largest AI neural networks are much smaller
in comparison.
Learning process: Brains learn continuously throughout life and can generalize
from very few examples. Neural networks typically require large amounts of
labeled data and focused training sessions.
Energy efficiency: The brain operates on about 20 watts of power—roughly the
same as a dim light bulb. AI neural networks often require massive computing
resources and electricity.
Adaptability: Our brains seamlessly integrate different types of information
(vision, sound, touch, memory, emotion) and can rapidly adapt to new situations.
Neural networks are typically specialized for specific tasks.
Consciousness: Perhaps most fundamentally, brains give rise to consciousness
and subjective experience. Neural networks, despite their sophistication,
don't have experiences or awareness.
So while the basic concept of interconnected processing units is shared,
artificial neural networks are simplified models that capture only some
aspects of brain function, and they operate quite differently in practice.
User: What are some tasks where neural networks still struggle compared to
human brains?
AI: Neural networks still struggle with several capabilities that human brains
handle effortlessly:
Common sense reasoning: Humans naturally understand that water flows downhill
or that dropping a glass might break it. Neural networks lack this intuitive
physics and causality understanding.
Transfer learning across domains: While humans can apply knowledge from one
area to completely different situations, neural networks typically struggle
to transfer skills between unrelated tasks.
Learning from minimal examples: Humans can learn new concepts from just one or
two examples. A child who sees a zebra once can identify zebras for life,
while neural networks typically need thousands of examples.
Dealing with ambiguity and context: Humans excel at understanding nuance,
sarcasm, humor, and social contexts. Neural networks often miss subtle cues
and contextual meanings.
Creative problem-solving: When faced with novel challenges, humans can
improvise solutions using creativity and lateral thinking. Neural networks
generally perform poorly on problems they weren't explicitly trained for.
Long-term planning and hierarchical thinking: Breaking down complex goals
into sub-goals and reasoning through multi-step processes remains challenging
for AI systems.
Understanding causality vs. correlation: Neural networks can identify patterns
but often struggle to distinguish whether one thing causes another or they
just happen to co-occur.
Robustness to adversarial examples: Tiny, imperceptible changes to input data
can completely fool neural networks, while humans remain unaffected by such
manipulations.
These limitations highlight that despite impressive progress, today's neural
networks still implement a relatively narrow slice of the human brain's
capabilities.
This progression reveals how models maintain context through a conversation and can explore a topic with increasing depth and nuance.
Practical Application: The Teaching Value of Errors
Interestingly, I've found that model errors can be just as instructive as their successes. When an AI system misunderstands you or produces an inappropriate response, it offers insight into how these systems process information and where their limitations lie.
Try deliberately ambiguous prompts and see how the model handles uncertainty:
User: How much would it cost?
AI: I notice your question is quite broad. To provide a helpful answer about
costs, I would need more specific information about what you're referring to.
This could be a product, service, project, or something else entirely. Could
you provide more details about what you're interested in knowing the cost of?
This reveals how models handle ambiguity and whether they ask clarifying questions rather than making unwarranted assumptions.
Beyond Text: Multi-Modal Exploration
As AI systems increasingly handle images, code, and other formats, try exploring these capabilities:
User: [Sharing a photo of garden plants] Can you identify any problems with
these plants and suggest solutions?
AI: Based on the image you've shared, I can see your tomato plants are showing
some concerning symptoms:
The yellowing leaves with brown spots on the lower portions of the plant suggest
early blight, a common fungal disease. I notice the discoloration follows a
distinct pattern of concentric rings within the brown patches.
The curling leaves on some branches could indicate either water stress or
possibly aphid damage—I can see some small insects on the underside of a few
leaves.
Here are my suggestions:
For the fungal issue:
- Remove and dispose of (don't compost) the most affected leaves
- Improve air circulation by pruning some inner branches
- Apply a copper-based fungicide labeled for vegetable gardens
- Water at soil level rather than overhead to keep foliage dry
For the possible aphids:
- Spray affected areas with insecticidal soap or neem oil
- Introduce beneficial insects like ladybugs if they're available in your area
Preventative measures:
- Apply mulch to prevent soil splash onto leaves
- Consider a regular preventative fungicide schedule during humid weather
- Rotate your tomato planting location next season
The plants otherwise look healthy with good new growth, so addressing these
issues promptly should help them recover.
User: I'm learning Python. Can you explain what this code does and how I might
improve it?
def calc(a, b):
res = 0
if a > 5:
res = a * b
else:
res = a + b
return res
data = [2, 7, 10, 4, 3]
results = []
for i in data:
val = calc(i, 2)
results.append(val)
print(results)
AI: This Python code:
1. Defines a function `calc(a, b)` that:
- Multiplies `a` and `b` if `a` is greater than 5
- Adds `a` and `b` if `a` is 5 or less
- Returns the result
2. Creates a list `data` with five numbers: [2, 7, 10, 4, 3]
3. Creates an empty list `results`
4. Loops through each number in `data`, applying the `calc` function with the
number and 2 as inputs
5. Appends each result to the `results` list
6. Prints the final `results` list
When run, this produces: [4, 14, 20, 6, 5]
(For numbers ≤ 5, it adds 2; for numbers > 5, it multiplies by 2)
Here's how you could improve it:
```python
def calculate(number, factor):
"""
Returns number * factor if number > 5, otherwise returns number + factor.
Args:
number: The primary number to operate on
factor: The secondary number for the operation
Returns:
The result of the conditional operation
"""
return number * factor if number > 5 else number + factor
data = [2, 7, 10, 4, 3]
results = [calculate(num, 2) for num in data]
print(results)
Improvements:
- More descriptive function and variable names
- Added docstring to explain the function's purpose
- Used a ternary operator for cleaner conditional logic
- Replaced the manual loop with a list comprehension
- Avoided the unnecessary intermediate variable assignment
This version is more concise, more readable, and follows Python best practices while doing exactly the same thing.
Conclusion: Dialogue as Discovery
The most effective way to understand AI capabilities isn't through reading documentation—it's through conversation. Each exchange reveals something about how these systems understand context, maintain coherence, reason through problems, and acknowledge limitations.
For newcomers to AI, my advice is simple: start talking. Ask questions, follow up, provide feedback, and pay attention to how the system responds. This dialogue-based exploration will teach you more about AI's practical uses and limitations than any technical manual ever could.
The future of human-AI interaction lies not in treating these systems as traditional tools but as conversation partners with unique capabilities and constraints. Understanding those nuances comes only through practice—so start the conversation today.
