Are basic Sentiment Analysis projects still impressive to employers in the age of ChatGPT and LLMs?

All answers to this question.

You are right that basic sentiment analysis is now a "solved" problem. To stand out in 2025, you should build a "Knowledge Base Chatbot" using RAG. This project proves you understand how to connect a vector database like Pinecone or Milvus with an LLM. It shows you can handle unstructured data and deal with modern "AI Engineering" challenges like prompt injection and hallucination checks. Recruiters are currently desperate for people who know how to bridge the gap between static enterprise data and dynamic AI responses.

   Answered 2025-06-12 by Megan Roberts

Megan, for a RAG project, is it better to use a paid API like OpenAI to get it working quickly, or does hosting a local model via Ollama show more technical depth to a hiring manager?

   Answered 2025-06-14 by Kevin Murphy

• Kevin, definitely go the local route if your hardware allows it! Hosting a model locally and managing the inference yourself shows you understand the infrastructure side of AI. It demonstrates that you're not just a "wrapper developer" who knows how to call an API, but a true Machine Learning enthusiast who can manage the compute resources and optimize model performance.

     Commented 2025-06-15 by Megan Roberts

Don't forget the data cleaning! If you can show a "Before and After" of how you cleaned a messy text dataset, that usually impresses me more than the actual model choice.

   Answered 2025-06-17 by Charles Reed

• Great point, Charles. In the real world, 80% of the job is data wrangling. Showing your regex skills and text normalization pipeline is a massive green flag for any lead data scientist.

     Commented 2025-06-18 by Deborah Lewis