Building with LLMs? Accuracy Is Only Half the Story!
Ishaan Reddy
ML Engineer | LLMs, RAG, Python | Shipping AI features that survive production
Building with LLMs? Accuracy Is Only Half the Story!
Working with LLMs and RAG systems has taught me that a good AI feature isn't just about generating impressive answers. It's about making those answers useful, reliable, and affordable.
Here are 4 things I always consider when building practical GenAI applications:
Retrieval Quality: The right context matters more than simply adding more documents.
Evaluation: Test real user questions, measure answer quality, and track hallucinations.
Latency: A brilliant answer isn't helpful if users have to wait too long.
Cost: Optimize retrieval, caching, and model selection to balance quality with expenses.
One important lesson: Not every problem needs an LLM. Sometimes, a simple search, SQL query, or traditional ML model is faster, cheaper, and more reliable.
The goal isn't to use AI everywhere. It's to use AI where it creates real value.
What's the biggest challenge you've faced with LLM applications — accuracy, latency, or cost?
#MachineLearning #GenerativeAI #LLM #RAG #ArtificialIntelligence #MLOps #DataScience
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