Kind: captions
Language: en
You've probably asked chat GPT or Bard
the same question twice and gotten
completely different answers. One time
it nails it, the other time it's
completely off. Frustrating, right?
Well, I spent months testing every major
AI tool and diving deep into prompt
engineering research from Google,
OpenAI, and Microsoft, and I found
something surprising. It's not the AI
that's inconsistent. It's how we're
asking the questions. The difference
between a terrible response and a genius
level answer often comes down to just a
few words. Welcome back to bitbiased.ai,
where we do the research so you don't
have to join our community of AI
enthusiasts with our free weekly
newsletter. Click the link in the
description below to subscribe. You will
get the key AI news, tools, and learning
resources to stay ahead. So, in this
video, I'm going to show you the exact
science behind prompt engineering, the
art of crafting AI inputs that
consistently give you brilliant outputs.
We'll cover everything from the
fundamentals that most people get wrong
to advanced techniques used by AI
researchers that can boost your results
by over 10%. By the end, you'll know
exactly how to engineer your prompts for
better, more reliable AI results every
single time. First up, let's talk about
what prompt engineering actually is and
why it matters so much more than you
think. What is prompt engineering?
Here's the thing about AI language
models. They're incredibly powerful, but
they're also incredibly literal.
Prompt engineering is essentially the
process of crafting and optimizing the
exact input you give a language model so
it produces the best possible output.
Think of it like designing a clear road
map for the AI to follow. Google Cloud
defines it as the art and science of
designing and optimizing prompts to
guide models towards the desired
responses.
And that word choice, art and science is
important. It's not just about being
polite to the AI or using magic words.
It's about strategically including the
right context, instructions, and
examples to steer the AI toward
understanding your intent and responding
meaningfully. By carefully writing our
prompts, we're essentially teaching the
AI what we want without having to
retrain the entire model.
And here's where it gets interesting.
This skill is becoming one of the most
valuable abilities in the AI age. Why
this matters more than you think? Now,
you might be wondering, can't I just ask
the AI normally and it'll figure out
what I mean? Well, here's the problem.
Large language models like chat GPT are
inherently stochastic, which is a fancy
way of saying they're somewhat random.
Small changes in how you word.
Something can lead to massive
differences in the answers you get.
Good prompt engineering tames that
randomness. For example, if we set
sampling controls like temperature, top
K, and top P wisely, the model's output
becomes far more reliable. Google's
guide points out that LLMs are
stochastic, not deterministic. You'll
get repeatable, better output when you
configure sampling controls like
temperature, top K, and top P in
combination, not an isolation. In
practice, that means we might reduce
temperature for precise tasks where we
need consistency or experiment with top
P to balance creativity and accuracy
when we're brainstorming. These aren't
just technical settings, they're levers
that give you control over the AI's
behavior. and once you understand them,
you'll never go back to default settings
again. The fundamentals, clarity and
structure. Let's start with the
foundation that most people completely
miss. First rule, always be clear and
specific. This sounds obvious, but you'd
be surprised how many people give vague,
rambling prompts and then wonder why the
AI gives vague, rambling answers.
Here's a pro tip that changed everything
for me. Put your instructions before any
context or data. OpenAI's documentation
emphasizes using separators like triple
quotes or hashtags to clearly mark off
your command from the content. For
example, instead of jumbling everything
together, structure it like this.
Summarize the text below as a bullet
point list. Text your input here. See
the difference? You're creating a clear
boundary between the instruction and the
content.
This helps the model see what to do and
what to process. It's like the
difference between someone handing you a
messy pile of papers versus a
wellorganized folder with clear labels.
But here's where most people stop short.
They're clear about what they want, but
not specific enough about how they want
it. Don't just say, "Write a poem." Say,
"Write a short, inspiring poem about AI
in the style of Maya Angelo." No more
than 12 lines. Being descriptive about
context, outcome, length, format, and
style dramatically improves your
results. And wait until you see this
next technique.
Open AAI shows that providing examples
of the desired output format helps the
model follow along.
In their examples, instead of just
saying extract entities, they show the
exact JSON or list format they want.
The model then matches that format
almost perfectly. It's like showing
someone a finished product before asking
them to build one. They have a clear
target to aim for.
Zero shot verse few shot. The game
changer. Now we're getting into
territory that separates casual users
from people who really understand how to
leverage AI.
Prompt engineering often uses what we
call shots. A zeroot prompt gives the
model just the task description and no
examples.
A few prompt includes one or more
example input output pairs up front.
Microsoft's guidance explains that fshot
prompts, those with examples, better
condition the model for the task, while
zeroshot means you're going in blind
with no examples.
So, which one should you use? Here's my
approach. Start simple with a zeros
prompt first, then add one to three
examples if the results aren't quite
there.
Google's guide even notes that few shot
examples aren't just filler. They're
essential for guiding structure, logic,
and tone. In other words, giving the
model a couple of solved examples in the
prompt shows it exactly how to answer so
it doesn't have to guess your intent.
I've seen fot prompting turn mediocre
results into exceptional ones with just
two well-chosen examples. But here's the
catch. Those examples need to be
realistic and representative. If you're
doing classification, make sure your
fshot examples cover the variety of
labels you're working with. Otherwise,
you're introducing bias before the AI
even starts. The power of context. This
next part will completely change how you
interact with AI. Always feed the model
relevant context. If you want a summary,
provide the text to summarize. If it's a
conversation, include the chat history
or the user's previous questions. This
seems basic, but Google's cloud docs
stress that providing context and
examples is critical for helping the AI
understand the task at a deeper level.
For multi-turn chats like chat GPT,
system enroll messages can set the
entire behavior. For instance, telling
it you are a friendly customer support
agent will make the tone more helpful
and approachable.
The Google guide explicitly separates
three layers. system instructions, what
the model should do, role, who it's
acting as, and context, the data it
should use. Setting these layers finally
tunes both tone and purpose. Think about
it. Prefixing your prompt with you are
an expert doctor versus you are a casual
friend will completely change how
detailed or technical the answer is. One
will give you medical terminology and
precise explanations. The other will
give you simple, relatable language.
Same question, totally different
outputs, all because of context.
Advanced techniques that actually work.
Once you have the basics down, there are
some more powerful tricks that can take
your prompts to the next level. Trust
me, these aren't just theoretical, they
produce measurably better results. Chain
of thought. This is where you encourage
the model to think step by step. Ask it
to explain its reasoning before giving a
final answer. For complex queries, this
often yields significantly better
accuracy. In fact, Google's guide calls
chain of thought prompting table stakes
for complex reasoning tasks. Instead of
asking what's the answer, ask what's the
answer? Show your reasoning step by
step. The difference is remarkable. Step
back prompting.
Here's a technique I love for really
difficult problems. Instead of having
the model solve the problem immediately,
first ask it for general principles or
an outline. For example, what are the
steps needed to solve this type of
problem?
Then use that outline to craft your
final query.
This meta level thinking can uncover
approaches you'd otherwise miss.
It's like stepping back from a painting
to see the whole picture before diving
into the details. Tree of thought. For
extremely hard problems, you can have
the model explore multiple solution
paths in parallel, basically branching
thought trees before deciding on the
best answer.
This mimics how humans tackle complex
challenges by considering multiple
approaches simultaneously.
Self-consistency.
This one's clever. Run a reasoning
prompt several times with high
temperature and take the most common
answer.
This voting method makes outputs more
robust and reduces random mistakes. It's
like getting multiple opinions before
making a decision, except the opinions
are all from different versions of the
AI's reasoning process. React, reason,
plus act.
This combines the model's internal
reasoning with external tools. For
example, the AI might search the web or
run code as intermediate steps. Google
notes react as a way to give LLMs a
basic agent-like workflow which
significantly improves real world
answers. The model isn't just thinking,
it's acting on those thoughts and
gathering more information.
JSON and structured output. When you
need structured data, lists, tables,
JSON,
explicitly tell the model to format its
answer accordingly.
For instance, output must be valid JSON
with field summary and keywords. The
guide even suggests including the JSON
template right in the prompt. This
vastly improves the model's ability to
give precise machine readable results
that you can immediately use in your
applications. Prompt variables. Use
placeholders like art name or arc city
in your prompts for reusability.
Then you can fill them programmatically
or in different scenarios.
This is incredibly powerful for
automating workflows or building a
prompt library that scales across your
organization. Automatic meta prompt
engineering. This is next level. Let the
model help design prompts for you. One
technique is to ask the AI to rewrite or
refine a prompt. Google's guide
describes using the model to generate
multiple prompt versions and then
selecting the best performing one. This
meta prompting can find creative ways to
ask your question that you might not
think of yourself.
It's like having an AI prompt engineer
working alongside you. Best practices
from the tech giants.
Now, let me share what Google, OpenAI,
Microsoft, and Meta all agree on when it
comes to prompt engineering best
practices.
These aren't just suggestions. They're
battle tested guidelines from the
companies building these models. Google
emphasizes treating prompting as
engineering, not guesswork. That means
being systematic and iterative. Test,
measure, refine, repeat. Always phrase
instructions positively. Say, "Do this
instead of don't do that."
The AI is much better at following
affirmative instructions than avoiding
things.
Respect token limits by breaking long
inputs into parts or summarizing to
avoid truncation.
Here's a sneaky one. When listing
choices like categories, randomize their
order to prevent positional bias. The AI
has a tendency to favor options that
appear first or last. So shuffling them
ensures fairer results. And here's the
reality check. No matter how good your
prompt is, you need to validate the
answer. Always include a final
verification step. The prompt gets you
90% of the way there, but human
oversight closes that last 10%. OpenAI's
best practices echo these ideas. They
advise using the latest model since
newer models are generally easier to
prompt and more capable.
Give clear, detailed instructions
upfront.
They show examples of how to separate
instructions, provide context, and
iteratively refine prompts. One tip that
saved me tons of frustration.
Reduce fluff in your prompt by replacing
vague terms like a few sentences with
exact constraints like 3 to five
sentences.
The more specific you are, the better
the results. They also explicitly
recommend starting with zero shot, then
adding few shot examples if needed, and
finally considering fine-tuning for
large-scale projects where you're
running thousands of similar prompts.
Microsoft's Azure documentation
introduces the concept of cues, short
prefix signals to jumpstart the AI's
output.
For example, adding a queue like key
points before a summary request can
nudge the model to list bullet points
automatically. These small signals have
an outsized impact on formatting. They
also emphasize that examples should be
realistic. In a classification task, few
shot examples should cover the variety
of labels to avoid bias.
Don't just show the easy cases. Include
edge cases and tricky examples that
teach the model how to handle ambiguity.
Even Meta's Llama team offers valuable
guidance through AWS blogs. They note
that the base llama models are quite
flexible in zeroshot and fshot mode
while their instruct variants use a more
structured conversational format. They
stress iterative refinement. Try prompts
with real data and tweak them based on
results.
Build, test, collect feedback and repeat
to ensure your prompts work reliably in
the real world, not just in perfect test
conditions.
Real world results that prove it works.
Now, let's talk results because these
strategies aren't just theory.
In actual experiments, prompt
engineering clearly boosts performance
on practical tasks. And the numbers are
pretty eye opening. One medical
education study gave chat GPT a mock
exam using carefully crafted prompts.
GPT 3.5 score jumped by 10.6% and GPT4s
increased by 3.2%.
That's a huge improvement just from
better prompting. Now, the latest GPT4
variants that are already heavily
optimized showed less improvement
because they're already operating near
ceiling accuracy, but the point stands.
Structured prompting matters most for
models that haven't fully internalized
the instructions yet. Google's prompt
guide walks through real coding tasks,
demonstrating how to translate bash
commands into Python code and how to
debug by having the model generate logs
and identify errors.
So in practice, you can use prompt
engineering for everything. Writing
creative content, summarizing documents,
generating code, debugging, formulating
SQL queries, or powering customer
service chat bots. I've personally used
these techniques to automate content
creation workflows, generate marketing
copy that actually converts, and even
build AI powered tools for data
analysis. The applications are
practically limitless once you master
the fundamentals.
Expert tips and bonus strategies. Let me
share some insider tips that experienced
prompt engineers use. First, test your
prompts across diverse inputs to catch
edge cases.
Don't just try the happy path. Throw
weird, unexpected inputs at it to see
where it breaks. That's where you learn
the most. Mix up how you phrase similar
requests. The AI can sometimes latch on
to one particular phrasing. So varying
your language helps ensure robust
performance across different scenarios.
Monitor for hallucinations. Those
moments when the AI confidently states
something completely false. If you
notice patterns in when hallucinations
occur, adjust your prompts to ground the
AI more firmly in provided context.
Iterate by analyzing failures.
If the model consistently overfits on a
certain label or misses a particular
type of question, shuffle your options
or rewrite the prompt for fairness.
Sometimes the smallest tweak, changing
one word or reordering examples can
eliminate a recurring problem.
And here's a technique I use constantly.
Leverage chat mode for refinement.
Sometimes interacting with the model
conversationally and asking follow-up
questions can refine the output much
better than trying to craft the perfect
prompt in one shot. It's okay to have a
conversation with the AI. That back and
forth often leads to better results than
any single prompt could achieve.
Summary and key takeaways. So, let's
bring this all together. Prompt
engineering is about thoughtful crafting
of your AI queries.
Start with a clear, specific
instruction. Provide relevant context
and examples. Format the output you want
explicitly. Tweak parameters like
temperature and max tokens based on your
needs. Use advanced techniques like
fshot examples for better conditioning,
chain of thought for complex reasoning
or meta prompting when you need help
designing the perfect prompt. Follow the
best practices from Google, OpenAI,
Microsoft and Meta. They've tested these
approaches at massive scale. Always
iterate and test your prompts in
realistic conditions with real world
data. With these methods, you'll
consistently engineer prompts that
produce better, more reliable AI
results.
It's not about luck or magic words. It's
about understanding how these models
think and structuring your requests
accordingly.
And the best part, these skills compound
over time. The more you practice, the
more intuitive it becomes.
Closing. Thanks for watching. If you
found this helpful, give it a like or
drop your own prompt engineering tips in
the comments below. I'm always learning
new techniques and I'd love to hear
what's working for you. With practice,
you'll get the most out of Chat GPT,
Bard, or any LLM simply by mastering how
you ask questions.
Remember, the future belongs to people
who know how to communicate with AI
effectively.
And now you're one of them. Happy
prompting.