The Journey to Smarter SQL - Building Reindeer's Lightning-Fast Autocomplete

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What is Reindeer Autocomplete?

Hey everyone, and welcome back to the Reindeer blog!

If you’ve been using our modern, AI-powered database IDE, Reindeer, you know we’re obsessed with making your database interactions as smooth and fast as possible. You might have already experienced our autocomplete feature, which feels almost psychic in how it suggests the next piece of your SQL query.

Reindeer is an AI-powered modern database IDE designed to streamline development. Our flagship feature is the intelligent SQL autocomplete, which uses large language models (LLMs) to suggest the next piece of a query.

The goal of this feature is simple: provide instant, context-aware SQL suggestions to boost developer speed. Achieving this “instant” feel, however, was a complex journey of iteration, moving from a naive solution to a highly optimized hybrid approach.

The Three Stages of Development

But let me tell you, getting to this point was a wild ride.

The story of Reindeer’s autocomplete is a classic tale of big ideas meeting the hard realities of the internet. The journey to our final product involved three distinct phases, each driven by user experience and performance demands.

Stage 1: The Naive Approach (Keystroke-by-Keystroke)

When we first started, we were excited about the sheer potential of modern AI. We figured, “Why complicate things? The model is smart enough to know what comes next.”

Our initial prototype was straightforward: for every single keystroke the user typed in the editor, we sent an API request to the LLM backend for a suggestion. Yes, we know. Looking back, it seems a little naive!

💥 The Predictable Breakdown

This approach quickly failed due to critical performance issues:

• API Latency: The delay between the request and the response resulted in jarring lag, severely disrupting the user’s typing flow. Autocomplete felt sluggish and unusable.
• Token Overload: Sending a request for every character was prohibitively expensive and rapidly consumed API rate limits, making the solution unscalable.
• The User Experience (UX) Disaster: Autocomplete should feel instant. Our first version felt sluggish, expensive, and frankly, like a broken feature.

We quickly realized that simply throwing an LLM at every character wasn’t a product—it was a proof of concept for “How not to build this.” We had to be smarter.

Stage 2: Implementing Standard Controls (Debouncing and Triggers)

Our next iteration was about introducing smart controls. We adopted industry-standard techniques to moderate the frequency of LLM calls:

1. Debouncing: We introduced a small delay (debounce) after a keystroke. The request was only sent if the user paused typing, dramatically reducing unnecessary calls.
2. Contextual Triggers: We limited requests to moments where intelligent completion was genuinely needed, such as when you press Space or Tab, or after specific delimiters like commas, indicating a logical break.

This stage was a vast improvement, resulting in a system that was faster, cheaper, and functional. But we still felt we could push it one step further.

Stage 3: The Reindeer Leap (Local Caching for Hyperspeed)

This is where the magic really happens and where we took a unique, strategic approach. Our core insight was simple: SQL is a relatively small, constrained language. Once you start a common pattern, the next few moves are very predictable.

Instead of waiting for the user to type and then asking the LLM, we flipped the script.

🧠 The Logic: Getting Suggestions Before You Need Them

1. When a request is sent (after the debounce/trigger check), we ask the LLM for the top three or four most likely next suggestions.

2. These multiple suggestions are received and cached locally in the client-side memory (the user’s browser).

3. As the user types the next keystroke or two, the system checks the local cache first.

   • Success: If the new input matches a cached suggestion, it is displayed instantly with zero network latency.
   • Failure/New Context: Only if the input does not match the cache will the system consider initiating a new, debounced LLM request.

Powered by Cerebras

A crucial piece of making this all work is our choice of inference provider. We use Cerebras to power our autocomplete feature. Cerebras’s lightning-fast inference speeds are essential for delivering the near-instant suggestions that make our caching strategy so effective. Their specialized AI hardware allows us to get those initial LLM predictions quickly enough that our local caching can take over seamlessly.

Technical Effectiveness

This hybrid approach leverages the massive intelligence of the LLM while mitigating its single largest drawback—latency—by exploiting the predictable structure of SQL.

• Near-Zero Latency: The majority of suggestions are served locally, providing a speed that feels native and instantaneous.
• Cost Efficiency: We utilize the LLM economically, gaining 3-4 possible suggestions for the price of a single API call, massively reducing token consumption.
• Superior UX: The final product is an autocomplete feature that enhances productivity by maintaining typing flow without jarring delays.

This final iteration proves that the best AI-powered tools are often those that use the AI’s incredible intelligence strategically and economically, rather than just constantly firing requests.

Happy querying, and let us know what you think of the lightning-fast autocomplete in Reindeer!