Tyler Profile picture
Oct 10 7 tweets 1 min read Read on X
how to use AI for market research (step by step breakdown):
step 1: analyze your content performance

grab a batch of your best performing content and a batch that flopped

split them into two sections in a doc and feed to an LLM

and have it analyze the differences between what performed vs what didn't

your content performance is valuable data

as an AI can identify patterns in what resonates with your audience vs what doesn't
step 2: use deep research to find direct audience quotes

give a description of your target market and have it search reddit, quora, any forums really

and have it pull exact quotes from people discussing their pain points, desires, objections, fears regarding your offer
step 3: analyze customer conversations

you likely have DMs, customer support tickets, or direct conversations with customers

feed these into an AI to summarize common trends and patterns
step 4: create one comprehensive market research report

combine everything:
- performance analysis from your content
- direct quotes from target market
- research patterns from customer conversations

now you have a semi data-driven report
now you can feed it feed it into AI to get better feedback on new content ideas

or generate content ideas that actually resonate with your audience

or even draft content with context on what works vs what doesn't

bc now it understands your audience because you fed it real context/data
to recap AI market research:

- analyze best vs worst performing content
- use deep research for direct audience quotes
- summarize patterns from customer conversations
- compile into one comprehensive report
- use report as context for AI-generated content

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More from @tyler_agg

Oct 9
how to build an AI automation (a step by step breakdown):
step 1: map out the manual task

before automating anything, document how you do it manually:

- what's the step-by-step process if a human were to do this?
- what data and information do you need at each step?
- what are the key decision points?
step 2: decide how much variance you want in outputs

this depends on the task type:

creative tasks (writing copy) = more variance allowed

predictable tasks (categorization, data entry) = less variance needed

as this determines what guardrails you'll need later on
Read 9 tweets
Oct 8
how to automatically scrape data from the internet (like a data engineer):
this is setting up systems that save information from the internet into organized databases

example: collecting TikTok videos, captions, and engagement metrics every day

and this data becomes the foundation for AI systems you build later on
the process for the tiktok example:

- tools like ScrapeCreators and Apify visit websites and extract the specific info you want
- saves everything to spreadsheets or databases automatically
- runs on whatever schedule you set (daily, weekly, hourly)
Read 9 tweets
Oct 7
there's so much content on how to build AI agents, but no one ever talks about the data engineering pipelines that support them

here's a thread going over the basics of data engineering:
the goal of data engineering:

- extract data from various sources
- transform it into structured format
- load into a data warehouse like Snowflake

and this structured data often is used context for AI systems to make personalized recommendations
why this matters for AI:

to build personalized AI systems, you need clean, structured data

as the more structured and labeled it is, the more granular/accurate context we can retrieve for an AI system
Read 10 tweets
Oct 2
how to reverse engineer any successful AI product:
step 1: understand the manual process

before diving into a technical analysis, figure out what human task this AI product is automating

> what would someone do manually to achieve the same result?
> what decisions need to be made?
> what data is required at each step?
> what is the most painful part of this task that people are paying to automate?
step 2: create your own technical hypothesis

based on your knowledge of AI fundamentals (embeddings, RAG, APIs, etc.)

sketch out how YOU would build this

don't overthink it - focus on the core workflow and data flow

this gives you a baseline to compare against
Read 8 tweets
Oct 1
how to discover AI business ideas that actually make money (step by step breakdown):
step 1: research industries with manual bottlenecks

use this prompt to understand where people are struggling:

"You're a 20-year veteran in the [INDUSTRY]. What tasks consume the most time daily? What repetitive work do you wish would disappear?"

test this across different sectors
step 2: reach out to real people in those industries

LinkedIn search for decision-makers and send this:

"I'm researching the [INDUSTRY] and would love insights from someone with experience.

Would you be interested in a quick call to ask about your daily daily operations? Happy to send $50 for your time"

(if you're in college you can just say it's for a school project lol)
Read 7 tweets
Sep 30
how to vibe code your AI project in 7 days (step by step breakdown):
day 1: map out your system design

don't jump straight into code - plan the complete workflow first

what steps does your tool need to get users from point A to point B?

create a document outlining each step, what data is needed, what decisions get made
note that this isn't about code or AI yet - it's pure logic

"step 1: user uploads document, step 2: extract key info, step 3: if condition X then do Y"

having a clear system design prevents building something that doesn't work and makes it much easier to build
Read 11 tweets

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