Not long ago, I was just a guy trying to make **search not suck**. My first attempts at AI-powered search? **Disasters.** If I had a dollar for every time I thought, “this is completely broken,” I’d be retired by now.

But let’s back up. Before I was deep into **AI-driven document retrieval**, I was maintaining old-school LAMP stacks, hacking together PHP scripts, and generally avoiding anything that smelled like machine learning. Then one day, I found myself looking at **Azure Search AI** and thinking, “How hard could this be?”

(Spoiler: **very**.)

Step One: “Let’s Just Index Some Data”

The first thing I did was take a dataset of **government reports** and dump them into Azure Search. Simple, right?


search_client = SearchClient(endpoint, index_name, AzureKeyCredential(api_key))
results = search_client.search("zoning regulations")

And… it worked. Technically. If you typed in the exact words that were in the document, you got results. But if you phrased it even slightly differently, you got **nothing**.

This was not AI. This was **a fancy version of CTRL+F.**

Facebook Post from This Era:

“If anyone needs me, I’ll be aggressively refreshing Azure docs trying to figure out why my ‘AI-powered’ search system is just a mediocre word-matching engine.”

At this point, my significant other Jole started giving me grief. “So let me get this straight,” she said. “You built AI-powered search, and it only works if you already know exactly what you’re looking for?”

Yeah. Not my finest moment.

Step Two: Discovering Semantic Search

Azure has this feature called **semantic search**, which promised to make search “intelligent.” The idea was that instead of just matching keywords, it would understand meaning. So I turned it on and tried something new:


query = "What are the current zoning rules?"
search_results = search_client.search(query, query_type="semantic", query_language="en")

This was the first time AI-powered search felt like **actual magic**. Instead of looking for “zoning rules” word-for-word, it understood that **“land use policies”** might be relevant.

For about five minutes, I felt like a genius.

Then I realized something was **seriously wrong**.

Lesson #1: AI Doesn’t Always Know What You Want

Some searches worked beautifully. Others? Completely off the rails. I searched for **“commercial zoning”** and got results about residential noise complaints. I searched for **“budget reports”** and got a document about school lunches.

That’s when I realized:

  • **Semantic search depends on good data.** If your documents are a mess, your results will be too.
  • **Ranking is a mystery.** Azure decides what’s relevant, and you have almost no control.
  • **AI-powered search still needs traditional search.** You can’t rely on one method alone.

Facebook Post from This Era:

“Azure Search AI is great! If you don’t mind randomly getting documents about dog licensing laws when searching for tax regulations.”

I distinctly remember texting my friend Bob that night, ranting about how AI search was **worse than useless.** His response? “You could’ve just asked me—I would’ve told you to skip straight to the part where it actually works.”

Gee, thanks, Bob.

Step Three: Hybrid Search (AKA, Making AI Less Dumb)

After weeks of frustration, I finally figured out the key: **combine traditional search with AI ranking.**

Instead of picking one method or the other, I used **both**:


query = "Zoning rules for commercial properties"
search_results = search_client.search(
    query, 
    query_type="semantic", 
    query_language="en", 
    search_mode="all"
)

Now, I had:

  • **Fast keyword matches** when the user was specific.
  • **Smart ranking** when the query was vague.

This was the first time I ran a search and thought, **“Okay, this actually works.”**

The next time I saw Jole, she hit me with, “So, does it actually find things now, or is this just a very expensive guessing machine?”

Thankfully, I finally had a decent answer.

What I Wish I Knew from the Start

If I could go back and save myself weeks of frustration, here’s what I’d tell past-me:

  1. **Semantic search is great—but only if your data is structured well.**
  2. **Always use hybrid search.** AI is good, but it still needs a fallback.
  3. **AI won’t fix bad search—it enhances good search.** Garbage in, garbage out.

Final Facebook Post from This Era:

“Spent a month trying to ‘train’ Azure Search AI, only to realize I just needed to stop expecting it to be psychic. Hybrid search for the win.”

Next Up: The Painful Introduction to Embeddings

At this stage, I had **basic AI-powered search working**, but I wasn’t done. Next, I started experimenting with **vector embeddings**—and let’s just say, it got worse before it got better.

More on that in the next post.

Have you struggled with AI-powered search? Let’s swap horror stories.