When it comes to AI-powered automation, the default mindset is often: Use an LLM for everything. Need to extract structured data? LLM. Need fuzzy matching? LLM. Need to process text? LLM.
But what if you don’t need an LLM for everything? What if you can achieve the same (or even better) results using a simpler, faster, and cheaper method?
This is the story of how I slashed my LLM cost by over 50% while building a voice-controlled automation system—by asking one simple question:
👉 How did people solve this before LLMs?
The Problem: Extracting Parameters from Voice Commands
I was working on an AI-powered software automation system designed to make user interactions seamless. The goal was simple:
✅ Let users speak a command instead of clicking through multiple screens.
✅ Automatically extract parameters (like amounts, names, and actions).
✅ Auto-navigate the UI to complete the task without manual effort.
For example, if a user said:
💬 “Transfer 5,000 PKR to Ahmed’s account.”
The system needed to extract:
- Action: Transfer
- Amount: 5,000 PKR
- Recipient: Ahmed
Seems straightforward, right? Not quite.
The Catch: Rare Parameters & High LLM Costs
Many of the parameters were rare terms—things like transaction types, banking actions, or UI elements. Even LLMs struggled to understand them accurately unless I explicitly provided all possible values.
To improve accuracy, I had to:
- Feed every possible option into the LLM’s prompt.
- Let the LLM compare the transcribed command against this list.
- Extract the best-matching parameter.
🔴 The Problem? This drastically increased token usage—and cost.
🔴 Scaling Issue? Almost every task in my system required this process.
If I continued down this path, my LLM costs would skyrocket—all for a problem that didn’t actually require an LLM.
The Breakthrough: Thinking Beyond LLMs
At this point, I took a step back and asked myself:
💡 Before LLMs, how did people solve fuzzy text matching problems?
That’s when I came across RapidFuzz—a Python library optimized for fuzzy string matching. Instead of using an LLM to extract and compare parameters, I could:
✅ Use RapidFuzz to compare words against predefined parameter lists.
✅ Handle misspellings, space variations, and partial matches efficiently.
✅ Avoid LLM calls altogether for this process.
Example: Handling Fuzzy Matches
Let’s say the user said “جائز”, but the actual parameter in my system was “ھبہ بحق جا ئز وارثان” (with extra spaces and different formatting).
🔴 LLM Approach:
- Feed all possible values into the prompt.
- Let the LLM process and extract the correct match.
- Consume expensive tokens in the process.
✅ RapidFuzz Approach:
- Use Levenshtein distance and token matching to find the closest match.
- Get the best result instantly—without an LLM.
The Impact: Faster, Cheaper, and Just as Accurate
By switching to fuzzy matching with RapidFuzz, I achieved:
🚀 50%+ reduction in LLM costs (by removing unnecessary API calls).
⚡ Faster processing (RapidFuzz runs in milliseconds).
🎯 High accuracy, even for rare or misspelled words.
💰 More scalable architecture (lower costs = better long-term viability).
The Lesson: Don’t Use a Hammer Where a Stick Will Do
In AI development, it’s easy to assume that LLMs are the best tool for every problem—but that’s not always the case. Sometimes, classic algorithms (like fuzzy matching) can solve a problem better, faster, and cheaper.
💡 Next time you face a similar challenge, ask yourself:
👉 How did people solve this before LLMs?
You might just find a better way to do it.
Final Thoughts
This experience reinforced an important lesson: AI is a tool, not a crutch. LLMs are powerful, but using them inefficiently can lead to high costs, slower performance, and unnecessary complexity.
By balancing AI-powered and traditional approaches, we can build more efficient, scalable, and cost-effective systems.
💡 Have you ever optimized an AI system in a surprising way? Let’s discuss in the comments! 👇