LLM Troubleshooting: Check Prompt, Metrics, Fallbacks, and Context

If you're building with LLMs and getting inconsistent results, check these before changing the model: → Is your system prompt actually specific, or is it just vibes? → Are you measuring outputs systematically, or eyeballing them? → Do you have a fallback for when the model is uncertain? → Are you chunking your context in a way that preserves meaning? I've fixed "bad model" complaints four times this year without changing the model once. The wrapper matters more than the weights. #AI #Python #developer #MachineLearning

If you're building with LLMs and getting inconsistent results, check these before changing the model: → Is your system prompt actually specific, or is it just vibes? → Are you measuring outputs systematically, or eyeballing them? → Do you have a fallback for when the model is uncertain? → Are you chunking your context in a way that preserves meaning? I've fixed "bad model" complaints four times this year without changing the model once. The wrapper matters more than the weights. #AI #Python #developer #MachineLearning

If you're building with LLMs and getting inconsistent results, check these before changing the model: → Is your system prompt actually specific, or is it just vibes? → Are you measuring outputs systematically, or eyeballing them? → Do you have a fallback for when the model is uncertain? → Are you chunking your context in a way that preserves meaning? I've fixed "bad model" complaints four times this year without changing the model once. The wrapper matters more than the weights. #AI #Python #developer #MachineLearning

If you're building with LLMs and getting inconsistent results, check these before changing the model: → Is your system prompt actually specific, or is it just vibes? → Are you measuring outputs systematically, or eyeballing them? → Do you have a fallback for when the model is uncertain? → Are you chunking your context in a way that preserves meaning? I've fixed "bad model" complaints four times this year without changing the model once. The wrapper matters more than the weights. #AI #Python #developer #MachineLearning

If you're building with LLMs and getting inconsistent results, check these before changing the model: → Is your system prompt actually specific, or is it just vibes? → Are you measuring outputs systematically, or eyeballing them? → Do you have a fallback for when the model is uncertain? → Are you chunking your context in a way that preserves meaning? I've fixed "bad model" complaints four times this year without changing the model once. The wrapper matters more than the weights. #AI #Python #developer #MachineLearning

If you're building with LLMs and getting inconsistent results, check these before changing the model: → Is your system prompt actually specific, or is it just vibes? → Are you measuring outputs systematically, or eyeballing them? → Do you have a fallback for when the model is uncertain? → Are you chunking your context in a way that preserves meaning? I've fixed "bad model" complaints four times this year without changing the model once. The wrapper matters more than the weights. #AI #Python #developer #MachineLearning

🚀 Just built a RAG AI Assistant! This tool lets users upload PDFs or text files and get context-aware answers instantly using Python, FastAPI, Sentence Transformers, Groq API, and LLaMA 3.1. Key Highlights: Semantic search for accurate and fast responses Handles multiple document formats Scalable and efficient backend 💻 Check it out: [https://lnkd.in/g9BmUMRD] 📝 Feedback and thoughts are welcome! #AI #MachineLearning #Python #FastAPI #RAG #OpenSource

🚀 Day 15 of My Generative & Agentic AI Journey! Today’s focus was on understanding Variable Scope in Python — how the same variable name can behave differently depending on where it is defined. Here’s what I learned: 🌍 Global vs Local Scope: • Variables defined outside a function are global • Variables inside a function are local 👉 Even if the variable name is the same (like student_name), the one inside the function is completely different from the one outside. 🔁 Nested Function Scope: • Functions can be defined inside other functions • Inner functions can have their own variables, even with the same name 👉 Example use case: A student_name defined in the outer function can be different from the one inside the inner function, and both don’t affect each other. 💡 Key takeaway: Scope controls where a variable can be accessed — understanding this avoids confusion and helps write bug-free code. Going deeper into how Python handles variables behind the scenes 🚀 #Day15 #Python #GenerativeAI #AgenticAI #LearningJourney #BuildInPublic

🎥 Here’s a quick demo of my Sentiment Analysis Web Application in action! This project predicts whether a given text is Positive, Negative, or Neutral using Machine Learning. 🔹 Built using Python, TF-IDF, and ML models 🔹 Integrated with a Flask web application 🔹 Deployed live using Render 👉 Try it here: https://lnkd.in/dVU2kzP8 I’ve also shared the project screenshots and code details in my previous post. Would love to hear your feedback! #MachineLearning #Python #Flask #DataScience #Projects #AI

Hey, Check out the demo video below to see Air draw in action! I’m looking forward to exploring how these gesture-based interfaces can be further integrated into everyday tools. #ComputerVision #Replit #Python #AI #HandTracking #Innovation #TechBuild