DEV Community

Cover image for Debugging AI Apps in Very Simple Way – Fix Real Problems in ChatGPT, LangChain, OpenAI
Meenakshi Agarwal
Meenakshi Agarwal

Posted on

Debugging AI Apps in Very Simple Way – Fix Real Problems in ChatGPT, LangChain, OpenAI

#ai #openai #debugging #llmapps

AI apps look cool from outside, but inside… sometimes, it’s messy.
You ask the chatbot something, and it replies weird. Or gives wrong data. Or crashes with no reason.

This article will help you debug your AI app step-by-step, in very simple language. No big theory. Just real problems and working fixes.

🧠 Problem 1: “AI reply is wrong or off-topic”

You send a prompt, but reply makes no sense. Or it's too random. Or not what you asked.

✅ What you can do:

✔️ A. Make your prompt more clear

Bad:

Summarize this article.
Enter fullscreen mode Exit fullscreen mode

Better:

You are a helpful AI assistant. Summarize the article in 2 lines. Keep it simple and clear. Do not copy full sentences.
Text: [paste text here]
Enter fullscreen mode Exit fullscreen mode

🟩 Tip: Always tell what kind of reply you want (length, tone, format, etc.)

✔️ B. Check temperature setting 

temperature: 1.0  // 🔥 Too random, risky
temperature: 0.3  // ✅ Stable, safe
Enter fullscreen mode Exit fullscreen mode

Low temperature = more correct answers
High temperature = more creativity, but sometimes wrong or funny replies

🪫 Problem 2: “AI reply is cut off halfway”

You expect a long answer, but it stops in middle. No error, just half reply.

✅ What to check:

✔️ A. Use higher max_tokens 

max_tokens: 4000  // allows longer reply
Enter fullscreen mode Exit fullscreen mode

But remember: tokens = input + output. If your prompt is already long, reply will be shorter.

✔️ B. Try counting tokens using this:

🔗 https://platform.openai.com/tokenizer

Paste your prompt and see how many tokens used.

🟨 Fix: Keep prompt short and to the point. Long prompt eats reply space.

🧩 Problem 3: “Chatbot forgets previous messages”

You build a chatbot, it works fine in first 2–3 messages. Then it forgets or repeats.

✅ Fix in 2 ways:

✔️ A. Summarize old chat into short memory 

You are a memory assistant. Read this chat and write a short memory:

User: Hello  
Bot: Hi!  
User: I like books  
Bot: Great! What kind?
...
Enter fullscreen mode Exit fullscreen mode

Now send this memory as context for next message.

✔️ B. Clean old history

Keep only last 5–10 messages. Don’t send full conversation every time.

🟦 Why: GPT has token limit (~4K or 8K). Old history can make it forget the latest input.

🔐 Problem 4: “API stops or crashes randomly”

Sometimes your app works, sometimes it breaks. You get errors like:

429: Too many requests  
500: Internal Server Error
Enter fullscreen mode Exit fullscreen mode

✅ What to do:

✔️ A. Add delay or retry 

for (let i = 0; i < 3; i++) {
  try {
    const res = await callOpenAI();
    break;
  } catch (err) {
    console.log("Trying again...", err);
    await wait(1000); // wait 1 sec
  }
}
Enter fullscreen mode Exit fullscreen mode

✔️ B. Check your usage

🔗 Go to https://platform.openai.com/account/usage
See if you are hitting the free limit or quota.

🪛 Problem 5: “LangChain gives no result or partial result”

LangChain is awesome, but sometimes the chain fails and you don’t know why.

✅ Use this:

✔️ A. Turn on verbose logs 

chain = new LLMChain({
  llm: model,
  prompt: prompt,
  verbose: true
});
Enter fullscreen mode Exit fullscreen mode

Now LangChain will show each step in console.

✔️ B. Add debug handlers 

const callbackManager = CallbackManager.fromHandlers({
  handleLLMStart: () => console.log("LLM started"),
  handleLLMEnd: () => console.log("LLM ended"),
});
Enter fullscreen mode Exit fullscreen mode

Now you can track where it stopped or what failed.

🧾 Problem 6: “AI should return JSON or list but gives plain text”

You want AI to return something like:

{
  "name": "Sarah",
  "age": 30
}
Enter fullscreen mode Exit fullscreen mode

But it gives:

Name: Sarah  
Age: 30
Enter fullscreen mode Exit fullscreen mode

✅ Solution:

✔️ A. Add output format in prompt 

You are a data bot. Always reply in JSON like this:

{
  "name": "John",
  "age": 24
}

Now, give info for Sarah, age 30.
Enter fullscreen mode Exit fullscreen mode

✔️ B. Use function_call in OpenAI

It forces AI to return in JSON.

🟢 Bonus: You can also add response_format: 'json' in OpenAI call if your model supports it.

📦 Bonus Tools You Can Use (Free)

Tool What it helps with
OpenAI Tokenizer Check token count
PromptPerfect Test + improve prompt
jsfiddle.net Test JS code with OpenAI
LangChain Verbose Mode See full logs
OpenAI Playground Try prompts easily

🧪 Copy-Paste Debug Checklist

✅ Check This 💬 Why It Matters
Prompt clear and detailed? AI needs proper task
Temperature low? Helps better results
Enough tokens set? Reply won't get cut
API not overloaded? Prevent rate error
LangChain verbose on? Debug steps easier
Output format shown? AI follows patterns

💬 Final Words (Simple Talk)

AI apps are smart but also tricky. Sometimes they break without error.
So you need to think like this:

  • Is my input clear?
  • Is AI confused?
  • Am I sending too much?
  • Do I need better logs?

Don’t worry. Everyone struggles.
Just follow these small steps, and you’ll fix most bugs in your LLM app.

💖 If this guide helped you, please drop a like and comment your debugging story.

Top comments (3)

Collapse
 
trojanmocx profile image
ALI •

Great tips! I’d also add: when chaining tools with LangChain, watch out for hidden token bloat in memory. Summarizing context is underrated! Thanks for simplifying debugging like a pro.

Collapse
 
meenakshi052003 profile image
Meenakshi Agarwal •

Great tip—token bloat in memory is easy to miss. Summarizing really helps. Glad you found the post useful!

Collapse
 
alexey_sokolov_10deecd763 profile image
Alechko •

Really clear and actionable guide. Your debugging checklist is literally what I needed when my LangChain broke for the 5th time today. One more thing I’d add: when chaining tools or memory managers, token bloat can sneak up ON YOU—summarizing old context early is clutch. If you ever want to pair that with a tool that captures real-time snapshots and bypasses the “oh but static code looks fine” trap, check out Element to LLM - made with love for devs.