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Hi, I’m a professor of cognitive science and design at UC San Diego, and I just lately wrote posts on Radar about my experiences coding with and talking to generative AI instruments like ChatGPT. In this put up I need to speak about utilizing generative AI to increase one in every of my educational software program initiatives—the Python Tutor software for studying programming—with an AI chat tutor. We typically hear about GenAI being utilized in large-scale business settings, however we don’t hear practically as a lot about smaller-scale not-for-profit initiatives. Thus, this put up serves as a case examine on including generative AI into a private undertaking the place I didn’t have a lot time, assets, or experience at my disposal. Working on this undertaking received me actually enthusiastic about being right here at this second proper as highly effective GenAI instruments are beginning to develop into extra accessible to nonexperts like myself.
Learn sooner. Dig deeper. See farther.
For some context, over the previous 15 years I’ve been working Python Tutor (https://pythontutor.com/), a free on-line software that tens of thousands and thousands of individuals world wide have used to write down, run, and visually debug their code (first in Python and now additionally in Java, C, C++, and JavaScript). Python Tutor is especially utilized by college students to grasp and debug their homework task code step-by-step by seeing its name stack and knowledge buildings. Think of it as a digital teacher who attracts diagrams to point out runtime state on a whiteboard. It’s greatest suited to small items of self-contained code that college students generally encounter in pc science lessons or on-line coding tutorials.
Here’s an instance of utilizing Python Tutor to step by a recursive perform that builds up a linked record of Python tuples. At the present step, the visualization exhibits two recursive calls to the listSum perform and numerous tips that could record nodes. You can transfer the slider ahead and backward to see how this code runs step-by-step:
AI Chat for Python Tutor’s Code Visualizer
Way again in 2009 after I was a grad scholar, I envisioned creating Python Tutor to be an automatic tutor that would assist college students with programming questions (which is why I selected that undertaking identify). But the issue was that AI wasn’t practically adequate again then to emulate a human tutor. Some AI researchers have been publishing papers within the discipline of clever tutoring methods, however there have been no broadly accessible software program libraries or APIs that may very well be used to make an AI tutor. So as a substitute I spent all these years engaged on a flexible code visualizer that may very well be *used* by human tutors to clarify code execution.
Fast-forward 15 years to 2024, and generative AI instruments like ChatGPT, Claude, and lots of others based mostly on LLMs (giant language fashions) at the moment are actually good at holding human-level conversations, particularly about technical subjects associated to programming. In specific, they’re nice at producing and explaining small items of self-contained code (e.g., below 100 traces), which is strictly the goal use case for Python Tutor. So with this expertise in hand, I used these LLMs so as to add AI-based chat to Python Tutor. Here’s a fast demo of what it does.
First I designed the person interface to be so simple as doable: It’s only a chat field under the person’s code and visualization:
There’s a dropdown menu of templates to get you began, however you may kind in any query you need. When you click on “Send,” the AI tutor will ship your code, present visualization state (e.g., name stack and knowledge buildings), terminal textual content output, and query to an LLM, which is able to reply right here with one thing like:
Note how the LLM can “see” your present code and visualization, so it may well clarify to you what’s occurring right here. This emulates what an knowledgeable human tutor would say. You can then proceed chatting back-and-forth such as you would with a human.
In addition to explaining code, one other frequent use case for this AI tutor helps college students get unstuck once they encounter a compiler or runtime error, which may be very irritating for newbies. Here’s an index out-of-bounds error in Python:
Whenever there’s an error, the software mechanically populates your chat field with “Help me fix this error,” however you may choose a distinct query from the dropdown (proven expanded above). When you hit “Send” right here, the AI tutor responds with one thing like:
Note that when the AI generates code examples, there’s a “Visualize Me” button beneath every one so as to straight visualize it in Python Tutor. This means that you can visually step by its execution and ask the AI follow-up questions on it.
Besides asking particular questions on your code, you can too ask normal programming questions and even career-related questions like easy methods to put together for a technical coding interview. For occasion:
… and it’ll generate code examples that you would be able to visualize with out leaving the Python Tutor web site.
Benefits over Directly Using ChatGPT
The apparent query right here is: What are the advantages of utilizing AI chat inside Python Tutor fairly than pasting your code and query into ChatGPT? I believe there are just a few fundamental advantages, particularly for Python Tutor’s audience of newbies who’re simply beginning to be taught to code:
1) Convenience – Millions of scholars are already writing, compiling, working, and visually debugging code inside Python Tutor, so it feels very pure for them to additionally ask questions with out leaving the location. If as a substitute they should choose their code from a textual content editor or IDE, copy it into one other web site like ChatGPT, after which possibly additionally copy their error message, terminal output, and describe what’s going on at runtime (e.g., values of knowledge buildings), that’s far more cumbersome of a person expertise. Some fashionable IDEs do have AI chat built-in, however these require experience to arrange since they’re meant for skilled software program builders. In distinction, the principle enchantment of Python Tutor for newbies has all the time been its ease of entry: Anyone can go to pythontutor.com and begin coding instantly with out putting in software program or making a person account.
2) Beginner-friendly LLM prompts – Next, even when somebody have been to undergo the difficulty of copy-pasting their code, error message, terminal output, and runtime state into ChatGPT, I’ve discovered that newbies aren’t good at arising with prompts (i.e., written directions) that direct LLMs to provide simply comprehensible responses. Python Tutor’s AI chat addresses this drawback by augmenting chats with a system immediate like the next to emphasise directness, conciseness, and beginner-friendliness:
You are an knowledgeable programming trainer and I’m a scholar asking you for assist with
${LANGUAGE}.
– Be concise and direct. Keep your response below 300 phrases if doable.
– Write on the stage {that a} newbie scholar in an introductory programming class can perceive.
– If you must edit my code, make as few adjustments as wanted and protect as a lot of my authentic code as doable. Add code feedback to clarify your adjustments.
– Any code you write needs to be self-contained and runnable with out importing exterior libraries.
– Use GitHub Flavored Markdown.
It additionally codecs the person’s code, error message, related line numbers, and runtime state in a well-structured method for LLMs to ingest. Lastly, it offers a dropdown menu of frequent questions and instructions like “What does this error message mean?” and “Explain what this code does line-by-line.” so newbies can begin crafting a query instantly with out looking at a clean chat field. All of this behind-the-scenes immediate templating helps customers to keep away from frequent issues with straight utilizing ChatGPT, such because it producing explanations which might be too wordy, jargon-filled, and overwhelming for newbies.
3) Running your code as a substitute of simply “looking” at it – Lastly, when you paste your code and query into ChatGPT, it “inspects” your code by studying over it like a human tutor would do. But it doesn’t really run your code so it doesn’t know what perform calls, variables, and knowledge buildings actually exist throughout execution. While fashionable LLMs are good at guessing what code does by “looking” at it, there’s no substitute for working code on an actual pc. In distinction, Python Tutor runs your code in order that if you ask AI chat about what’s occurring, it sends the true values of the decision stack, knowledge buildings, and terminal output to the LLM, which once more hopefully ends in extra useful responses.
Using Generative AI to Build Generative AI
Now that you just’ve seen how Python Tutor’s AI chat works, you is likely to be questioning: Did I take advantage of generative AI to assist me construct this GenAI function? Yes and no. GenAI helped me most after I was getting began, however as I received deeper in I discovered much less of a use for it.
Using Generative AI to Create a Mock-up User Interface
My method was to first construct a stand-alone web-based LLM chat app and later combine it into Python Tutor’s codebase. In November 2024, I purchased a Claude Pro subscription since I heard good buzz about its code era capabilities. I started by working with Claude to generate a mock-up person interface for an LLM chat app with acquainted options like a person enter field, textual content bubbles for each the LLM and human person’s chats, HTML formatting with Markdown, syntax-highlighted code blocks, and streaming the LLM’s response incrementally fairly than making the person wait till it completed. None of this was progressive—it’s what everybody expects from utilizing a LLM chat interface like ChatGPT.
I favored working with Claude to construct this mock-up as a result of it generated stay runnable variations of HTML, CSS, and JavaScript code so I may work together with it within the browser with out copying the code into my very own undertaking. (Simon Willison wrote a nice put up on this Claude Artifacts function.) However, the principle draw back is that each time I request even a small code tweak, it might take as much as a minute or so to regenerate all of the undertaking code (and typically annoyingly depart components as incomplete […] segments, which made the code not run). If I had as a substitute used an AI-powered IDE like Cursor or Windsurf, then I’d’ve been capable of ask for immediate incremental edits. But I didn’t need to trouble organising extra complicated tooling, and Claude was adequate for getting my frontend began.
A False Start by Locally Hosting an LLM
Now onto the backend. I initially began this undertaking after enjoying with Ollama on my laptop computer, which is an app that allowed me to run LLMs domestically at no cost with out having to pay a cloud supplier. A number of months earlier (September 2024) Llama 3.2 had come out, which featured smaller fashions like 1B and 3B (1 and three billion parameters, respectively). These are a lot much less highly effective than state-of-the-art fashions, that are 100 to 1,000 instances larger on the time of writing. I had no hope of working bigger fashions domestically (e.g., Llama 405B), however these smaller 1B and 3B fashions ran superb on my laptop computer so that they appeared promising.
Note that the final time I attempted working an LLM domestically was GPT-2 (sure, 2!) again in 2021, and it was TERRIBLE—a ache to arrange by putting in a bunch of Python dependencies, superslow to run, and producing nonsensical outcomes. So for years I didn’t assume it was possible to self-host my very own LLM for Python Tutor. And I didn’t need to pay to make use of a cloud API like ChatGPT or Claude since Python Tutor is a not-for-profit undertaking on a shoestring finances; I couldn’t afford to supply a free AI tutor for over 10,000 day by day lively customers whereas consuming all of the costly API prices myself.
But now, three years later, the mix of smaller LLMs and Ollama’s ease-of-use satisfied me that the time was proper for me to self-host my very own LLM for Python Tutor. So I used Claude and ChatGPT to assist me write some boilerplate code to attach my prototype internet chat frontend with a Node.js backend that known as Ollama to run Llama 1B/3B domestically. Once I received that demo engaged on my laptop computer, my aim was to host it on just a few college Linux servers that I had entry to.
But barely one week in, I received unhealthy information that ended up being an enormous blessing in disguise. Our college IT of us instructed me that I wouldn’t be capable to entry the few Linux servers with sufficient CPUs and RAM wanted to run Ollama, so I needed to scrap my preliminary plans for self-hosting. Note that the sort of low-cost server I needed to deploy on didn’t have GPUs, so that they ran Ollama far more slowly on their CPUs. But in my preliminary assessments a small mannequin like Llama 3.2 3B nonetheless ran okay for just a few concurrent requests, producing a response inside 45 seconds for as much as 4 concurrent customers. This isn’t “good” by any measure, however it’s one of the best I may do with out paying for a cloud LLM API, which I used to be afraid to do given Python Tutor’s sizable userbase and tiny finances. I figured if I had, say 4 reproduction servers, then I may serve as much as 16 concurrent customers inside 45 seconds, or possibly 8 concurrents inside 20 seconds (tough estimates). That wouldn’t be one of the best person expertise, however once more Python Tutor is free for customers, so their expectations can’t be sky-high. My plan was to write down my very own load-balancing code to direct incoming requests to the lowest-load server and queuing code so if there have been extra concurrent customers making an attempt to attach than a server had capability for, it might queue them as much as keep away from crashes. Then I would wish to write down all of the sysadmin/DevOps code to observe these servers, preserve them up-to-date, and reboot in the event that they failed. This was all a frightening prospect to code up and check robustly, particularly as a result of I’m not an expert software program developer. But to my reduction, now I didn’t need to do any of that grind for the reason that college server plan was a no-go.
Switching to the OpenRouter Cloud API
So what did I find yourself utilizing as a substitute? Serendipitously, round this time somebody pointed me to OpenRouter, which is an API that enables me to write down code as soon as and entry quite a lot of paid LLMs by altering the LLM identify in a configuration string. I signed up, received an API key, and began making queries to Llama 3B within the cloud inside minutes. I used to be shocked by how straightforward this code was to arrange! So I shortly wrapped it in a server backend that streams the LLM’s response textual content in actual time to my frontend utilizing SSE (server-sent occasions), which shows it within the mock-up chat UI. Here’s the essence of my Python backend code:
import openai # OpenRouter makes use of the OpenAI API, so run"pip set up openai" first shopper = openai.OpenAI(base_url="https://openrouter.ai/api/v1",
api_key=
)completion = shopper.chat.completions.create(
mannequin=, messages=, stream=True)for chunk in completion:textual content = chunk.selections[0].delta.content material
OpenRouter does price cash, however I used to be keen to provide it a shot for the reason that costs for Llama 3B regarded extra affordable than state-of-the-art fashions like ChatGPT or Claude. At the time of writing, 3B is about $0.04 USD per million tokens, and a state-of-the-art LLM prices as much as 500x as a lot (ChatGPT-4o is $12.50 and Claude 3.5 Sonnet is $18). I’d be scared to make use of ChatGPT or Claude at these costs, however I felt comfy with the less expensive Llama 3B. What additionally gave me consolation was understanding I wouldn’t get up with a large invoice if there have been a sudden spike in utilization; OpenRouter lets me put in a set amount of cash, and if that runs out my API calls merely fail fairly than charging my bank card extra.
For some further peace of thoughts I carried out my very own fee limits: 1) Each person’s enter and whole chat conversations are restricted to a sure size to maintain prices below management (and to scale back hallucinations since smaller LLMs are inclined to go “off the rails” as conversations develop longer); 2) Each person can ship just one chat per minute, which once more prevents overuse. Hopefully this isn’t a giant drawback for Python Tutor customers since they want no less than a minute to learn the LLM’s response, check out urged code fixes, then ask a follow-up query.
Using OpenRouter’s cloud API fairly than self-hosting on my college’s servers turned out to be so significantly better since: 1) Python Tutor customers can get responses inside only some seconds fairly than ready 30-45 seconds; 2) I didn’t have to do any sysadmin/DevOps work to keep up my servers, or to write down my very own load balancing or queuing code to interface with Ollama; 3) I can simply attempt totally different LLMs by altering a configuration string.
GenAI as a Thought Partner and On-Demand Teacher
After getting the “happy path” working (i.e., when OpenRouter API calls succeed), I spent a bunch of time eager about error situations and ensuring my code dealt with them nicely since I needed to supply a very good person expertise. Here I used ChatGPT and Claude as a thought companion by having GenAI assist me provide you with edge instances that I hadn’t initially thought-about. I then created a debugging UI panel with a dozen buttons under the chat field that I may press to simulate particular errors to be able to check how nicely my app dealt with these instances:
After getting my stand-alone LLM chat app working robustly on error instances, it was time to combine it into the principle Python Tutor codebase. This course of took numerous time and elbow grease, however it was simple since I made positive to have my stand-alone app use the identical variations of older JavaScript libraries that Python Tutor was utilizing. This meant that firstly of my undertaking I needed to instruct Claude to generate mock-up frontend code utilizing these older libraries; in any other case by default it might use fashionable JavaScript frameworks like React or Svelte that may not combine nicely with Python Tutor, which is written utilizing 2010-era jQuery and buddies.
At this level I discovered myself probably not utilizing generative AI day-to-day since I used to be working inside the consolation zone of my very own codebase. GenAI was helpful firstly to assist me determine the “unknown unknowns.” But now that the issue was well-scoped I felt far more comfy writing each line of code myself. My day by day grind from this level onward concerned numerous UI/UX sharpening to make a easy person expertise. And I discovered it simpler to straight write code fairly than take into consideration easy methods to instruct GenAI to code it for me. Also, I needed to grasp each line of code that went into my codebase since I knew that each line would must be maintained maybe years into the long run. So even when I may have used GenAI to code sooner within the brief time period, which will have come again to hang-out me later within the type of delicate bugs that arose as a result of I didn’t absolutely perceive the implications of AI-generated code.
That stated, I nonetheless discovered GenAI helpful as a substitute for Google or Stack Overflow types of questions like “How do I write X in modern JavaScript?” It’s an unbelievable useful resource for studying technical particulars on the fly, and I typically tailored the instance code in AI responses into my codebase. But no less than for this undertaking, I didn’t really feel comfy having GenAI “do the driving” by producing giant swaths of code that I’d copy-paste verbatim.
Finishing Touches and Launching
I needed to launch by the brand new yr, in order November rolled into December I used to be making regular progress getting the person expertise extra polished. There have been 1,000,000 little particulars to work by, however that’s the case with any nontrivial software program undertaking. I didn’t have the assets to judge how nicely smaller LLMs carry out on actual questions that customers may ask on the Python Tutor web site, however from casual testing I used to be dismayed (however not shocked) at how typically the 1B and 3B fashions produced incorrect explanations. I attempted upgrading to a Llama 8B mannequin, and it was nonetheless not superb. I held out hope that tweaking my system immediate would enhance efficiency. I didn’t spend a ton of time on it, however my preliminary impression was that no quantity of tweaking may make up for the truth that a smaller mannequin is simply much less succesful—like a canine mind in comparison with a human mind.
Fortunately in late December—solely two weeks earlier than launch—Meta launched a new Llama 3.3 70B mannequin. I used to be working out of time, so I took the simple method out and switched my OpenRouter configuration to make use of it. My AI Tutor’s responses immediately received higher and made fewer errors, even with my authentic system immediate. I used to be nervous in regards to the 10x value improve from 3B to 70B ($0.04 to $0.42 per million tokens) however gave it a shot anyhow.
Parting Thoughts and Lessons Learned
Fast-forward to the current. It’s been two months since launch, and prices are affordable thus far. With my strict fee limits in place Python Tutor customers are making round 2,000 LLM queries per day, which prices lower than a greenback every day utilizing Llama 3.3 70B. And I’m hopeful that I can change to extra highly effective fashions as their costs drop over time. In sum, it’s tremendous satisfying to see this AI chat function stay on the location after dreaming about it for nearly 15 years since I first created Python Tutor way back. I like how cloud APIs and low-cost LLMs have made generative AI accessible to nonexperts like myself.
Here are some takeaways for many who need to play with GenAI of their private apps:
- I extremely suggest utilizing a cloud API supplier like OpenRouter fairly than self-hosting LLMs by yourself VMs or (even worse) shopping for a bodily machine with GPUs. It’s infinitely cheaper and extra handy to make use of the cloud right here, particularly for personal-scale initiatives. Even with hundreds of queries per day, Python Tutor’s AI prices are tiny in comparison with paying for VMs or bodily machines.
- Waiting helped! It’s good to not be on the bleeding edge on a regular basis. If I had tried to do that undertaking in 2021 through the early days of the OpenAI GPT-3 API like early adopters did, I’d’ve confronted numerous ache working round tough edges in fast-changing APIs; easy-to-use instruction-tuned chat fashions didn’t even exist again then! Also, there wouldn’t be any on-line docs or tutorials about greatest practices, and (very meta!) LLMs again then wouldn’t know easy methods to assist me code utilizing these APIs for the reason that needed docs weren’t obtainable for them to coach on. By merely ready just a few years, I used to be capable of work with high-quality steady cloud APIs and get helpful technical assist from Claude and ChatGPT whereas coding my app.
- It’s enjoyable to play with LLM APIs fairly than utilizing the online interfaces like most individuals do. By writing code with these APIs you may intuitively “feel” what works nicely and what doesn’t. And since these are peculiar internet APIs, you may combine them into initiatives written in any programming language that your undertaking is already utilizing.
- I’ve discovered {that a} brief, direct, and easy system immediate with a bigger LLM will beat elaborate system prompts with a smaller LLM. Shorter system prompts additionally imply that every question prices you much less cash (since they have to be included within the question).
- Don’t fear about evaluating output high quality when you don’t have assets to take action. Come up with just a few handcrafted assessments and run them as you’re growing—in my case it was difficult items of code that I needed to ask Python Tutor’s AI chat to assist me repair. If you stress an excessive amount of about optimizing LLM efficiency, then you definately’ll by no means ship something! And if you end up craving for higher high quality, improve to a bigger LLM first fairly than tediously tweaking your immediate.
- It’s very laborious to estimate how a lot working an LLM will price in manufacturing since prices are calculated per million enter/output tokens, which isn’t intuitive to purpose about. The greatest option to estimate is to run some check queries, get a way of how wordy the LLM’s responses are, then have a look at your account dashboard to see how a lot every question price you. For occasion, does a typical question price 1/10 cent, 1 cent, or a number of cents? No option to discover out except you attempt. My hunch is that it in all probability prices lower than you think about, and you’ll all the time implement fee limiting or change to a lower-cost mannequin later if price turns into a priority.
- Related to above, when you’re making a prototype or one thing the place solely a small variety of folks will use it at first, then undoubtedly use one of the best state-of-the-art LLM to point out off probably the most spectacular outcomes. Price doesn’t matter a lot because you received’t be issuing that many queries. But in case your app has a good variety of customers like Python Tutor does, then choose a smaller mannequin that also performs nicely for its value. For me it looks like Llama 3.3 70B strikes that steadiness in early 2025. But as new fashions come onto the scene, I’ll reevaluate these price-to-performance trade-offs.