---
title: 'Lesson 7: Effective prompting techniques (Deep Dive) | AI Fluency: Framework & Foundations Course'
source: 'https://youtube.com/watch?v=2YCaBqP8muw'
video_id: '2YCaBqP8muw'
date: 2026-06-16
duration_sec: 0
---

# Lesson 7: Effective prompting techniques (Deep Dive) | AI Fluency: Framework & Foundations Course

> Source: [Lesson 7: Effective prompting techniques (Deep Dive) | AI Fluency: Framework & Foundations Course](https://youtube.com/watch?v=2YCaBqP8muw)

## Summary

This video explores the practical skill of crafting effective prompts for AI assistants like Claude. It emphasizes that prompting is simply clear communication—explaining what you want, how you want it done, and how you want to interact. The video covers six foundational tips and the importance of iteration and experimentation.

### Key Points

- **Prompting as clear communication** [0:24] — Prompting is simply clearly communicating what we want, how we want it done, and how we want to interact with the AI assistant.
- **Six foundational prompting tips** [1:54] — The six tips are: give context, show examples, specify output constraints, break complex tasks into steps, ask the AI to think first, and define its role/style/tone.
- **Tip 1: Give context** [2:17] — Be specific about what you want, why you want it, and who you are. Example: adding background about a job interview tailors the response.
- **Tip 2: Show examples** [3:34] — Providing examples of desired output (few-shot prompting) helps the AI emulate the style you want.
- **Tip 3: Specify output constraints** [4:53] — Specify format, length, language, or design elements to get exactly what you need.
- **Tip 4: Break complex tasks into steps** [5:36] — Breaking complex requests into steps (chain-of-thought prompting) helps the AI follow your process.
- **Tip 5: Ask the AI to think first** [6:51] — Asking the AI to think through a problem before answering leads to more thorough responses.
- **Tip 6: Define role, style, or tone** [7:53] — Specify the AI's role, expertise level, or communication style to guide its approach.
- **Secret weapon: Ask AI to improve your prompt** [8:47] — Describe your issue to the AI and ask it to help craft a better prompt.
- **Iteration and experimentation** [9:17] — Prompting is iterative; refine by adding context, examples, or breaking tasks into steps. Ask for variations or reset the conversation.

## Transcript

[Music]
Let's explore one of the most practical
skills when working with AI. Crafting
effective prompts. This might sound
technical or complicated, and some
guides certainly make it seem that way,
but at its heart, it's surprisingly
straightforward. Prompting is simply how
we apply this course's description
competency in practice. clearly
communicating what we want, how we want
it done, and how we want to interact
with our AI assistant throughout the
entire process. Think of prompting like
explaining a task to a helpful new
colleague who's eager to assist, but
needs clear directions and expectation
setting to do their best work. We'll be
using Claude throughout this section,
but these tips can be carried over to
many other AI systems. You might have
heard the term prompt engineering tossed
around. Prompt engineering is simply the
practice of designing effective
instructions for AI systems like Claude.
It's about crafting your questions and
providing context in ways that help AI
assistants understand exactly what you
want. What's fascinating is that
effective prompting blends familiar
human communication skills with a few
considerations specific to AI. Many
principles that make for good human
conversation, such as being clear,
providing relevant context, and giving
concrete examples, also apply when
working with AI. Yet, there are
differences, such as being more explicit
about things humans could naturally
infer, and accommodating the AI's
limited context window, and sometimes,
depending on the AI you're working with,
using specific formatting that machines
can easily process. As AI assistants
continue to evolve, prompting best
practices evolve, too. What works with
today's AI systems may be different from
what works with tomorrow's.
Experimentation is key to discovering
what works best for your specific needs.
In this video, we'll mainly explore six
foundational prompting tips that will go
a long way toward helping you
effectively communicate and collaborate
with Claude and other AI systems. They
are give Claude context, show examples
of what good looks like, specify output
constraints, break complex tasks into
steps, ask Claude to think first, and
define Claude's role, style, or tone.
The first principle is simple but
powerful. Be specific and clear about
what you want, why you want it, and
perhaps most surprisingly, who you are.
Let's take a simple prompt. Tell me
about climate change. How can we improve
this by giving Cloud more context? A
more specific contextrich version could
look like, explain three major impacts
of climate change on agriculture in
tropical regions with examples from the
past decade. Our baseline prompt was
vague and leaves clawed guessing about
our interests, level of knowledge, and
the depth of detail we're looking for,
such as geography and time span. We can
even add more context by providing
information not just about what we're
looking for, but why we're asking and
how we'll be using that information that
Claude gives us. Now, our prompt looks
like this. Explain three major impacts
of climate change on agriculture in
tropical regions with examples from the
past decade. I'm preparing for a job
interview at an agricultural research
lab in Indonesia. I have a degree in
ecology, but no specific knowledge on
climate change. write a summary of key
concepts that would help me speak
intelligently in the interview. All this
added context helps tailor Claude's
response to your specific situation and
knowledge level. This kind of background
information is something we naturally
provide in human conversations, but
might forget to include when talking
with Claude. Sometimes showing is better
than telling. Providing examples of the
kind of output you're looking for can be
incredibly effective. This is sometimes
called fshot prompting or nshot
prompting in technical circles where n
is the number of examples given but it's
really just about showing the AI
examples for it to emulate. For
instance, take the following prompt.
Please convert this technical statement
to plain language. The platform
implements end-to-end encryption
protocols to safeguard data integrity.
Clog may already be able to do this to
your satisfaction. So we definitely
recommend you just try first without
examples and see where it leads you. But
let's say you have a very specific style
you want Claude to follow, and it's
harder to explain than to give examples.
Your refreshed prompt could look
something like this. Here are two
examples of how to convert technical
jargon into plain language. Original,
the quantum algorithm exhibits quadratic
speed up. Plain, the new method solves
problems roughly twice as fast as
previous methods. Original, the
interface leverages intuitive design
paradigms. Plain, the design is easy to
understand and use. Now, please convert
this complex technical manual to plain
language. When providing examples, aim
to cover the full diversity of possible
prompts, such as examples that cover
different cases or styles. This helps
Claude better understand the broad range
of the pattern you want it to follow.
Being clear about output constraints,
such as the desired format and length of
Claude's response, or the language you
want Claude to code in, or the color of
the buttons on the web page you want
Claude to design, also helps ensure you
get exactly what you need. Here's an
example of clear and detailed
description to ensure Claude delivers
exactly what you're looking for. Create
a clean, modern, single page art
portfolio website. Include these main
sections: hero, about me, skills,
portfolio projects experience and
contact. Make the navigation menu sticky
and responsive with hamburger menu on
mobile. Use a sunset color palette and
add a dark light mode toggle in the
navigation. Guidance like this helps
cloud structure its response to match
your expectations. When you have a
complicated request, breaking it down
into smaller steps helps Cloud follow
your thinking and deliver better
results. Think about it this way. If you
ask a friend to do something for you
without specifying how, there's a chance
that they may not do it the way you
intended them to. We've all been there.
Listing out task steps ensures that
Claude follows the process you want to
in order to accomplish its task. This is
sometimes called chain of thought
prompting. For example, instead of
asking Claude to analyze this quarterly
sales data, you might say, "I'd like to
analyze this quarterly sales data.
Please approach this by looking through
our sales records to identify the top
performing products, comparing current
quarter results to the previous quarter,
highlighting any unusual trends or
patterns, and then suggesting possible
reasons for these trends. By default,
you may not need to do this, especially
for tasks that are relatively
straightforward. Furthermore, modern
reasoning models or extended thinking
models are increasingly capable of
performing step-by-step reasoning on
their own, but you can still guide this
process to ensure it aligns with your
needs. The more variance there is in
ways to execute the task well, or the
more that proper task execution relies
on experience and knowledge you've
gained as a domain expert, the more you
should consider taking the time to
translate that knowledge into Claude.
Relatedly, sometimes it can be helpful
to explicitly give AI assistants like
Claude space to work through its process
first before executing its task. This
approach helps Claude produce more
thorough and well-considered responses.
For example, you can add this to your
prompt. Before answering, please think
through this problem carefully. Consider
the different factors involved,
potential constraints, and various
approaches before recommending the best
solution. As I mentioned, modern
reasoning or extended thinking models by
default think before acting. But if
you're working with an AI assistant that
does not think first by default, you can
still prompt the AI to do so. I want to
note the importance of giving the AI
assistant space to think before doing
its task, not after. If you want that
thinking to increase the quality of the
AI's work, just like how having space to
think before you act is different than
acting first, then being asked to
explain your thinking afterwards. As a
side benefit, this also allows you to
better see where the AI assistant might
be going astray and thus where you could
hone your description competency further
by providing more guidance. Specifying
how you want cloud to communicate and
behave can significantly change how it
approaches a task. By specifying the
level of expected expertise, the
perspective you want it to take, or its
communication style, you can guide both
Claude's interaction with you and the
final result of what it produces. Simply
put, who do you want the AI to act as?
For example, take this prompt. Please
explain how rainbows form from the
perspective of an experienced science
teacher speaking to a bright 10-year-old
who's interested in science. This is
also a good way to brainstorm or get
feedback. You can specify a general role
or even ask Claw to take on the persona
of a specific figure, such as Richard
Fineman, when asking for physics
explanations. Here's another example. As
a UX design expert, review this website
wireframe and suggest three improvements
focusing on user navigation and
accessibility. Perhaps the most powerful
technique is asking Claude to help
improve your prompt. When you're not
sure how to ask for something or how to
improve your prompt, describe to Claude
your issue or situation and ask it to
make your prompt better or write your
prompt for you. I'm trying to get you,
Claude, to help me with goal. I'm not
sure how to phrase my request to get the
best results. Can you help me craft an
effective prompt for this? Here's where
Claude and other AI assistants may vary
most in terms of performance. So, we
suggest you experiment with different
models as part of practicing delegation.
Effective prompting is iterative and
experimental. AI systems and best
practices are constantly evolving. So,
what works today may change tomorrow.
Your first attempt won't always yield
the perfect result, and that's expected.
When a response isn't quite what you
need, try refining your approach by
playing around with any of the
techniques we mentioned, such as add
more specificity or context. Provide
examples of your desired output. Break
the task into smaller steps and try a
different technique or combination of
techniques. You can also ask for
variations such as, "Can you give me
three different versions of this?" You
can request different formats such as,
"Instead of a paragraph, could you
present this in an interactive
artifact?" Note that artifacts are a
unique way that Claude can create
outputs that may be easier to understand
or more interesting to digest. You can
also check confidence, such as for
factual questions, you can ask, "How
confident are you about this answer?"
You can also reset the conversation
entirely. Sometimes starting a fresh
conversation gives better results than
trying to correct the conversation
that's gone off track. Use each
interaction as feedback to improve your
next prompt. Over time, you'll develop
an intuition for how to communicate
effectively with all AI systems. As you
apply these techniques in practice,
here's some guidance to recap. Some
patterns consistently work well.
Starting with a clear task overview
statement, including format
specifications and examples, setting
explicit constraints or requirements,
providing rich and relevant background
information, and common mistakes to
avoid are assuming that Claude can read
your mind, or overloading a single
prompt or conversation with multiple
unrelated tasks, being too vague about
what success looks like, and not
providing feedback on previous
responses. To recap, effective
communication with AI systems like
Claude combines timeless human
communication principles with AI
specific techniques. The approaches
we've covered will serve you well across
different AI systems. These six
principles together with the secret
weapon of asking Cloud for help form a
solid toolkit for applying the
description competence to your AI
interactions. Iteration and practice
here is the key to Swift improvement and
mastery. Remember that prompt
engineering is an evolving practice. As
models improve, some specific techniques
become less necessary. However, these
principles of good communication are
still relevant even if the way we apply
them changes. Maintain a spirit of
experimentation and adapt your approach
based on your results.