Talking Shop with Lane Wagner: How Boot.dev Develops AI Features
I recently had the chance to sit down with Lane Wagner, the founder of boot.dev, to talk about how they're using AI to create a better learning experience for coders (850K users). My background is in data science and AI, but I used boot.dev to learn more full-stack skills to be able to ship and deliver products. And now as a course creator on the platform (keep an eye out for my upcoming RAG course), I've seen how they're integrating technology to help people learn.
I was personally excited about this for several reasons. First, I know Lane’s work well and he makes great content. Second, as someone who is creating a course, hearing more about how AI is used in education is very valuable. And finally, my interest is in helping people incorporate AI into their careers and real products - hearing details from people “on the ground” in industry lets me make our course more practical. Here’s a 30% discount link for our course.
My philosophy has always been that you don't just learn to code and then go code; you learn to code so you can code to learn more. It's a continuous journey, and Lane and his team are building tools that lean right into that idea. Here's a look into our conversation about what it really takes to build useful AI features.
Boot.dev is a fantastic platform and way to learn backend development skills. I took it myself as a paying student and completed the full curriculum. If you’re interested in backend development to help ship products, sign up here for a discount.
What is boot.dev?
Boot.dev is an online curriculum focused on backend development. You learn languages like Python and Go through hands-on, in-browser coding exercises.
The platform features a built-in AI chatbot, "Boots," that has full context of what you're working on—from your code editor to the output window—to help you when you run into a wall.
But just having a chatbot doesn’t make for a fantastic experience. It's what you feed it that makes the chatbot valuable.
The Secret is "Context Engineering"
When AI APIs first became widely available, the boot.dev team immediately integrated a chatbot. Lane candidly shared that their first version was basic:
“It was 3.5 turbo at the time. And…good luck. Just copy and paste what you want to know about into the chat.” - Lane
They quickly realized the key to making the AI helpful was providing the right context. This process of "context engineering" has been their main focus.
Iteration 1: Add the current lesson content to the AI's context.
Iteration 2: Add the student's code from the editor.
The Next Big Step: Giving the AI a much broader, more personalized view of the student's journey. This included past lessons they struggled with, previous conversations with the AI, and even mastery scores on specific topics.
Lane's big takeaway?
"Giving the right context to the LLM is like all the work." - Lane
Without it, you get "AI slop." With it, the chat feels truly personalized. He also noted that just dumping everything into the context window isn't the answer. Bigger context windows are slower, more expensive, and can often lead to worse results. The goal is to provide the minimum amount of relevant information.
This gives a bit of an idea of the amount of work going into creating the best context.
"we're doing like basically every permutation of different type of challenge that you can generate in boot dev. We have a list of examples that we've handcrafted. So that we get the highest quality results from it. It's pretty tedious work, but I mean, if you want good performance, you got to put in that work." — Lane [20:56]
Managing Context Windows
"And in most cases, it actually makes the results worse. You really do just want to give it kind of the minimum amount of relevant information...The attention is focused on the thing that you believe is important for the LLM to think about as it's giving answers to the student." — Lane [06:55]
One of the most counterintuitive lessons from Boot.dev's experience is that more context often produces worse results. While modern LLMs boast massive context windows, filling them indiscriminately degrades performance in three ways
Responses become slower
Costs increase
The model's attention becomes diluted across irrelevant information.
Lane shared an example from their development process. Initially, they dumped 100,000 tokens of user data into the context, including records of all 3,000 lessons on the platform marked as "not completed." This massive context made the AI less helpful. The solution was aggressive curation to determine what information genuinely impacts the quality of responses and to exclude everything else.
Is It Actually Working? Feedback Over Metrics
So how do they know if a change is an improvement? Lane said their most valuable insights have been qualitative.
While they use a simple thumbs-up/thumbs-down system on messages to compare models like GPT-4, Claude Sonnet, and Gemini Flash, the biggest insight comes from their active Discord community.
“When students are getting weird or unhelpful responses from Boots, they'll paste a link to the conversation into the discord, and then we just go investigate. A single one-sentence negative example added to the system prompt, based on real user feedback, can often make a huge improvement.” - Lane
It's a great reminder that you can't replace simply looking at how people are using your product.
The Future: Infinite, AI-Generated Practice
One of the most exciting new features the team is building is the idea of "infinite practice." Handcrafting high-quality courses and practice problems takes an immense amount of time. Some students need three practice problems for a concept; others might need ten.
This is where AI can be extremely helpful. By giving a model a set of high-quality, handmade challenges as examples, boot.dev will be able to generate new, personalized challenges on the fly.
This creates a powerful feedback loop: students can rate the AI-generated challenges, and the boot.dev team can curate the best-rated ones become positive examples for future generations, while the worst-rated ones become negative examples.
Using the Right Tool for the Job
We also touched on tool calling, which Lane described simply as "a for loop and it's some tool calls." It's a powerful feature that allows the AI to access specific information only when needed.
For example, instead of cluttering every single prompt with details about boot.dev's pricing or game mechanics, they created a tool. Now, if a student asks about pricing, the system prompt tells the AI to "call this tool," which fetches the relevant documentation. The information is available on-demand without slowing down or adding cost to every single interaction.
Lane's rule of thumb, or "AI code smell," is this:
"If it starts every conversation by calling the same tools... you did something bad there. You should just be calling those tools before the conversation starts." - Lane
Tools are for optional, specific tasks, not for routine information gathering you do every time.
Why Backend Devs Need to Know AI
Why is boot.dev, a backend development platform, teaching courses on AI agents and Retrieval-Augmented Generation (RAG)? Lane's answer was clear: the skills required to be a backend developer are constantly changing.
“I think it's a core competency for backend developers in 2025 to be able to integrate with AI APIs and use them effectively” - Lane
Just as developers are expected to know how to integrate with services like Stripe for payments or SendGrid for email, they now need to understand how to work with AI. It's just another powerful tool in the modern developer's toolkit.