How to Learn Efficiently: Learning New Skills in Days or Hours

One of the biggest pitfalls people fall into when learning new technology is trying to learn everything upfront. While thorough understanding has its merits, in the real world, you don’t always have the luxury to go deep before diving in. Instead, focusing on understanding the basics and knowing where and when to apply a technology can empower you to start making meaningful contributions quickly. Let me walk you through my approach, including how I applied it in a real-world scenario.

1. The Basics: Learning Just Enough to Start

When I approach new technology, like WebSockets, my goal isn’t to become an expert immediately. Here’s what I focus on:

• The Essentials: I learn what the technology is and why it exists.
• Where and How to Use It: I study common use cases, identifying where it could fit in my projects.
• Alternatives, Pros, and Cons: Understanding other options helps in deciding if it’s the best choice.

This high-level overview doesn’t take long, and it sets a solid foundation without overwhelming me with details. I usually read a few articles or watch a tutorial to gain a practical perspective.

2. Putting It into Practice: Real-Time Streaming with WebSockets

To show you how I applied this approach, here’s what happened at Ben Inc. The team was facing serious latency issues in their AI-driven communication system, which included generative AI, text-to-speech (TTS), and automatic speech recognition (ASR). Their system was built using HTTP requests for data transfer, meaning each component (LLM, TTS, and ASR) operated sequentially, resulting in long response times of 7–10 seconds. This was too slow to feel “real-time” for users.

Identifying the Problem and Suggesting WebSockets

With my foundational understanding of WebSockets, I recognized an opportunity to streamline the data flow by replacing HTTP with WebSocket technology for real-time message streaming. WebSockets allow for persistent, low-latency connections, which would enable streaming responses rather than waiting for a full message to process and return.

Implementing the Solution Step-by-Step

1. Streaming the LLM Data: Instead of waiting for the language model (LLM) to generate the entire output before sending it to TTS, we could now stream data as it was generated. This meant users would start receiving audio output from the TTS almost immediately after the LLM began processing.
2. In-Transit TTS Processing: With WebSockets, data could be sent to TTS as soon as parts of the LLM response were ready, eliminating the bottleneck. Text was converted into speech on-the-go, removing the need to wait until all processing was complete.
3. Reducing Round-Trip Delays: By holding a continuous connection with WebSockets, we avoided the repeated request-response cycle of HTTP, further reducing delays. The real-time nature of WebSockets allowed us to deliver the response within 0.5 seconds—practically instantaneous compared to the initial 7–10 seconds.

This transformation took our response time to less than 0.5 seconds, a speed that rivals even human responses. Achieving this result didn’t require exhaustive knowledge of WebSockets before implementing; I learned specific intricacies as they came up during integration.

3. Learning and Implementing Simultaneously

By taking this approach, I gained a thorough, hands-on understanding of WebSockets that went beyond what I could have learned through study alone. Applying the technology to real problems:

• Built Expertise in Context: I gained knowledge directly tied to solving a pressing need.
• Kept Me Agile: I avoided the pitfall of over-preparation, making me quicker to adapt and deliver results.
• Focused on Practical Application: The primary goal was to address latency issues, and learning WebSockets “just enough” helped me achieve that efficiently.

4. The Takeaway: Start with Basics, Dive Deeper as Needed

The key is to filter information—focus on what’s essential to understand the basics and use it practically. You’ll always have the opportunity to learn advanced details as you need them, but starting with fundamentals and building depth through experience is what keeps you agile and effective.

You don’t need exhaustive knowledge to make an impact—just the right knowledge at the right time.